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Electroencephalography, Basic Principles, Clinical Applications and Related Fields
Abstract
Established in 1982 as the leading reference on electroencephalography, Drs. Niedermeyer's and Lopes da Silva's text is now in its thoroughly updated Fifth Edition. An international group of experts provides comprehensive coverage of the neurophysiologic and technical aspects of EEG, evoked potentials, and magnetoencephalography, as well as the clinical applications of these studies in neonates, infants, children, adults, and older adults. This edition includes digital EEG and advances in areas such as neurocognition. Three new chapters cover the topics of Ultra-Fast EEG Frequencies, Ultra-Slow Activity, and Cortico-Muscular Coherence. Hundreds of EEG tracings and other illustrations complement the text.
... EEG remains a cornerstone in epilepsy research due to its ability to capture the brain's electrical activity non-invasively [21]. However, challenges such as muscle artifacts, ocular movements and electrical noise can compromise the accuracy of seizure detection algorithms [7]. ...
... The preprocessing steps included filtering, epoch extraction, baseline removal and re-referencing, which collectively contributed to improved signal clarity and artifact reduction. Figures 2 and 3 illustrate the frequency bandwidth during seizure activities, aligning with the expected range of 3-30 Hz and highlight the role of specific frequency bands in seizure dynamics [21]. ...
... Effective but less precise; often requires additional tools [1,2] EEG remains a cornerstone in epilepsy research due to its ability to capture the brain's electrical activity non-invasively [21]. However, challenges such as muscle artifacts, ocular movements and electrical noise can compromise the accuracy of seizure detection algorithms [7]. ...
Objectives: Naturally, there are several challenges, such as muscular artifacts, ocular movements and electrical interferences that depend on precise diagnosis and classification, which hamper exact epileptic seizure detection. This study has been conducted to improve seizure detection accuracy in epilepsy patients using an advanced preprocessing technique that could remove such noxious artifacts. Methods: In the frame of this paper, the core tool in the area of epilepsy, EEG, will be applied to record and analyze the electrical patterns of the brain. The dataset includes recordings of seven epilepsy patients taken by the Unit of Neurology and Neurophysiology, University of Siena. The preprocessing techniques employed include advanced artifact removal and signal enhancement methods. We introduced Peak-to-Peak Amplitude Fluctuation (PPAF) to assess amplitude variability within Event-Related Potential (ERP) waveforms. This approach was applied to data from patients experiencing 3–5 seizures, categorized into three distinct groups. Results: The results indicated that the frontal and parietal regions, particularly the electrode areas Cz, Pz and Fp2, are the main contributors to epileptic seizures. Additionally, the implementation of the PPAF metric enhanced the effectiveness of seizure detection and classification algorithms, achieving accuracy rates of 99%, 98% and 95% for datasets with three, four and five seizures, respectively. Conclusions: The present research extends the epilepsy diagnosis with clues on brain activity during seizures and further demonstrates the effectiveness of advanced preprocessing techniques. The introduction of PPAF as a metric could have promising potential in improving both the accuracy and reliability of epilepsy seizure detection algorithms. These observations provide important implications for control and treatment both in focal and in generalized epilepsy.
... In contrast, actual limb movement typically manifests in the beta/gamma frequency region. [4] Event-Related Desynchronization (ERD) refers to a temporary decrease in the power of a specific brain wave frequency band at the onset of an internally or externally cued event. ...
... (3. 4) The training process in an artificial neural network aims to adjust the weights and biases within the network to minimize the mean squared error (MSE) between the actual output and the predicted output (refer to Eq (3.4)). This iterative process of error minimization is known as backpropagation. ...
This thesis presents a contribution to the field of humanoid robots and artificial intelligent technologies. The applications of humanoid robots in real-world scenarios are not yet widely known to the general public. The thesis proposes a methodology for humanizing humanoid robots using Artificial Neural Networks (ANNs) for EMG and EEG control. The GUCnoid 1.0 humanoid robot's performance and perception were enhanced through ANNs to generate movements. The ANNs were trained after applying EMG and EEG experiments to control a robotic arm and eye mechanism, achieving an accuracy above 90%. The trained ANNs produce normalized percentages for each class ("Thumbs up," "Y-letter," and "hand at rest") for the EMG experiment and ("Awareness" and "Drowsiness") states for the EEG experiment. The experiments were designed at the ARAtronics Research Center, where 10 participants participated at them. The Myo armband was used as the data collection device for the EMG experiment, and a developed EEG kit at the ARAtronics Research Center was used for data collection in the EEG experiment. The output percentages for each class produced by the ANNs will be multiplied by the control signals to generate control signals that produce generative movements even with the same pattern. This result is applied for a real-time Human-Robot Interaction (HRI) application on GUCnoid 1.0. A Brain-Computer Interface (BCI) application was also implemented, achieving two significant advantages. The first advantage of the BCI is the implementation of a user-friendly application for various users, especially commercial users unfamiliar with machine learning or coding syntax. The second advantage of the BCI is that it opens the field for developing ANN accuracy for new data, allowing for continuous adaptation. This integrated system is named Branoid in this thesis.
... Electroencephalogram (EEG) signals are commonly used by scientists to diagnose neurological diseases such as seizures. It is a technique that records brain electrical activity by placing electrodes on the scalp to capture electrical signals generated by neuronal activity [4]. These electrical signals reflect the brain's functional state and information processing, making EEG an important tool for studying brain function [5]. ...
... Epileptic seizure detection has seen substantial progress with the rise of machine learning models, particularly those using EEG signals. EEG tests record the electrical activity produced by neurons, typically non-invasively, by placing multiple electrodes on the scalp [4]. EEGs play a crucial role in detecting epileptic foci and categorizing epilepsy types, such as focal, generalized and unknown seizures [13][14][15]. ...
The landscape of artificial intelligence (AI) research is witnessing a transformative shift with the emergence of the Kolmogorov–Arnold network (KAN), presenting a novel architectural paradigm aimed to redefine the structural foundations of AI models, which are based on multilayer perceptron (MLP). Through rigorous experimentation and evaluation, we introduce the KAN–electroencephalogram (EEG) model, a tailored design for efficient seizure detection. Our proposed network is tested and successfully generalized on three different datasets, one from the USA, one from Europe, and one from Oceania, recorded with different front-end hardware. All datasets are scalp EEG in adults and are from patients living with epilepsy. Our empirical findings reveal that while both architectures demonstrate commendable performance in seizure detection, the KAN model exhibits high-level out-of-sample generalization across datasets from diverse geographical regions, underscoring its inherent efficacy and adaptability at the backbone level. Furthermore, we demonstrate the resilience of the KAN architecture to model size reduction and shallow network configurations, highlighting its versatility and efficiency by preventing over-fitting in-sample datasets. This study advances our understanding of innovative neural network architectures and underscores the pioneering potential of KANs in critical domains such as medical diagnostics.
... EEG measures electrical activity produced by neuronal firings in the human brain, typically recorded at the scalp using specialized sensors [20,40]. German physiologist and psychiatrist Hans Berger recorded the first human EEG in 1924 for medical purposes, which remains one of its primary applications [1,17,18]. ...
... Table 8 summarizes the state-of-the-art in multi-session brainwave authentication studies. As shown, previous studies typically involve [40][41][42][43][44][45][46][47][48][49][50][51][52][53][54] subjects and 80-270 sessions, which is significantly lower than our dataset of 345 subjects and 6013 sessions. From the perspective of time intervals between sessions, prior works vary: some datasets span a few days, such as those in [6,8], which report inconsistent EER values ranging from 1.32% to 11.99%. ...
The field of brainwave-based biometrics has gained attention for its potential to revolutionize user authentication through hands-free interaction, resistance to shoulder surfing, continuous authentication, and revocability. However, current research often relies on single-session or limited-session datasets with fewer than 55 subjects, raising concerns about generalizability and robustness. To address this gap, we conducted a large-scale study using a public brainwave dataset of 345 subjects and over 6,000 sessions (averaging 17 per subject) recorded over five years with three headsets. Our results reveal that deep learning approaches outperform classic feature extraction methods by 16.4\% in Equal Error Rates (EER) and comparing features using a simple cosine distance metric outperforms binary classifiers, which require extra negative samples for training. We also observe EER degrades over time (e.g., 7.7\% after 1 day to 19.69\% after a year). Therefore, it is necessary to reinforce the enrollment set after successful login attempts. Moreover, we demonstrate that fewer brainwave measurement sensors can be used, with an acceptable increase in EER, which is necessary for transitioning from medical-grade to affordable consumer-grade devices. Finally, we compared our findings with prior work on brainwave authentication and industrial biometric standards. While our performance is comparable or superior to prior work through the use of Supervised Contrastive Learning, standards remain unmet. However, we project that achieving industrial standards will be possible by training the feature extractor with at least 1,500 subjects. Moreover, we open-sourced our analysis code to promote further research.
... In the Table 2-1, the global classification in the different frequency bands is presented [48,12]. Considering that EEG signals acquire measurements in a non-invasive way, the devices are portable and low cost compared to other acquisition methods such as Magnetoencephalography (MEG) or functional Near-Infrared Spectroscopy (fNIRS) [51]. Several methodological strategies based on BCI can be implemented for the motor restoration of lost movements in people with disabilities through the identification of their intentions using EEG [16]. ...
... Different internal or external factors can affect the morphophysiology of EEG signals, generating behaviors not related to the patterns of a specific task. Among the most common factors are blinking, muscle activation, eye movement, cardiac activation, line power, among others [51]. In addition, recent studies have revealed that other factors such as concentration, eye fatigue, coffee consumption, and user experience can generate alterations in the signals to the point of affecting the performance in the identification of commands [24,45], limiting the application of rehabilitation systems. ...
Motor Imagery (MI) of simple tasks such as left and right hand movement, hand opening and closing, and foot or tongue movement has been deeply studied in the literature. Despite the great findings so far, according to the potential use for rehabilitation, there are still many challenges in the scientific community focused on the exploration of more tasks and protocols focused on MI of complex movements, as well as the use of robotic devices for motor assistance considering Activities of Daily Living (ADLs). However, Electroencephalography (EEG)-MI based paradigms have not yet been fully explored in the literature. This master dissertation aims at exploring complex MI tasks assisted mainly by an upper limb exoskeleton and a first-person 2D virtual reality. For this, the perspective from simple MI to complex MI tasks, including those assisted by robotic systems, was evaluated. For simple MI tasks (ST-Set dataset), a public database containing left and right hand MI was used. On the other hand, for exoskeleton MI taks (ET-Set dataset) a proprietary database of 10 healthy subjects was recorded combining MI together with assisted arm flexion and extension movement at two different speeds (30 rpm and 85 rpm). Finally, for MI of complex tasks (CT-Set dataset) a proprietary database of 30 healthy subjects and 7 post-stroke patients was recorded, assisting MI with a first-person 2D virtual reality for the generation of the Action Observation (AO). Different computational techniques were evaluated, including three supervised methods based on Common Spatial Patterns (CSP), two unsupervised method approaches based on Riemannian Geometry (RG), and three variations of methods based on Deep Learning (DL). Additionally, two classifiers Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) were evaluated for the supervised and unsupervised methods. Furthermore, two strategies for window segmentation were evaluated. As results, potential performance was found using the DL methods with Accuracy (ACC) and False Positive Rate (FPR) of approximately 0.6 and 0.45 for ST-Set, 0.98 and 0.05 for ET-Set, and 0.95 and 0.06 for CT-Set (0.8 and 0.22 for post-stroke patients), respectively. Next, RG achieved high performance levels with ACC and FPR of approximately 0.75 and 0.25 for ST-Set, 0.9 and 0.15 for ET-Set, and 0.73 and 0.27 for CT-Set (0.7and 0.3 for post-stroke), respectively. Finally, the CSP-based methods presented low performance with ACC and FPR of 0.55 and 0.49 for all three datasets. The results allow us to conclude that the presented methodologies of complex MI tasks, as well as the implemented computational variations, are feasible and suitable for the design and implementation of more robust Brain Computer Interface (BCI) systems, allowing a more impactful neurorehabilitation for ADLs recovery in post-stroke patients. In addition, improvements in Human-Machine Interaction (HMI) can be derived, generating increases in restoration due to improvements in usability, controllability and reliability of processes. The novel approaches presented here leave the door open to explore new paradigms, allowing to study the brain effects that occur during these tasks, in order to increase the understanding of the Central Nervous System (CNS).
... functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) have been explored for their potential applications in neurosurgery. EEG measures electrical activity on the scalp, providing valuable information about brain function but with limited spatial resolution (21). fMRI allows for non-invasive imaging of brain activity by measuring blood flow changes associated with neural activity, while MEG provides high temporal resolution for tracking brain dynamics (22). ...
... Despite the advantages of neural decoding, several limitations exist. For example, the spatial resolution of non-invasive methods like EEG is often insufficient for precise localization of neural activity, which can impact the accuracy of surgical planning and intervention (21,20). Furthermore, invasive techniques such as ECoG pose risks of complications, including infection and damage to brain tissue (26,27). ...
Robotic neurosurgery has significantly advanced surgical precision and outcomes, with personalization becoming crucial in optimizing treatment for individual patients. The crucial aspect of personalization has become increasingly apparent in optimizing treatment for individual patients, allowing for tailored and precise interventions that can significantly improve patient care and recovery. This literature review explores the integration of neural decoding and artificial intelligence (AI) in personalized robotic neurosurgery, focusing on current techniques, applications, and future directions. Neural decoding, which interprets neural signals to guide surgical interventions, is examined alongside its limitations, including spatial resolution and invasiveness. The review highlights how AI and machine learning enhance neural decoding by improving pattern recognition and predictive capabilities, thus enabling real-time adaptations during surgery. Personalized robotic neurosurgery leverages advanced imaging and real-time data to tailor surgical approaches, improving precision and reducing complications. The integration of neural decoding and AI into robotic systems presents significant benefits, such as enhanced accuracy and personalized care, but also faces challenges related to technology integration, cost, and reliability. Future research should address these challenges by developing robust algorithms and expanding clinical applications. This review provides a comprehensive overview of how combining these technologies can advance personalized neurosurgical practices and improve patient outcomes.
... However, CRP is just one among numerous biomarkers existing that collectively offer a comprehensive snapshot of the status of the various body systems. Others include for example heart rate, heart rate variability (Shaffer & Ginsberg, 2017), immune status (e.g., interleukin-6 [IL-6], interleukin-8 [IL-8], interleukin-1 beta , tumor necrosis factor alpha [TNF-alpha]) (Dinarello, 2018;Kany, Vollrath, & Relja, 2019;Tanaka, Narazaki, & Kishimoto, 2014), catecholamines and hormones such as cortisol, oxytocin, norepinephrine (Carter, 2014;Chrousos & Gold, 1992;Sapolsky, Romero, & Munck, 2000), microbiomes (Caspani et al., 2024;Kargbo, 2023;Kelly et al., 2023;Lloyd-Price, Abu-Ali, & Huttenhower, 2016;Thursby & Juge, 2017), telomeres (Blackburn, Epel, & Lin, 2015), electroencephalogram (EEG) (Biasiucci, Franceschiello, & Murray, 2019;Niedermeyer, 2011), and blood pressure (i.e., systolic and diastolic) (Whelton et al., 2018). This array of biomarkers allows for nuanced evaluations of various physiological parameters that provide valuable insights into cardiovascular health, stress levels, immune responses, cellular ageing, brain activity, and more that could facilitate personalized health management and intervention strategies. ...
Background
Research has shown that psychedelics may have therapeutic potential in treating mental disorders like depression and anxiety. However, the mechanisms and actions underlying their effects are still not fully understood. Similarly, while the significance of mindset and setting in shaping psychedelic experiences and therapeutic outcomes is well established, information about the influence of the body is comparatively scarce.
Aim
This paper introduces the concept of bodyset, defined as the state of the body, including both the body and brain. We suggest it as a vital element in preparing for psychedelic experiences and beyond, broadening the traditional ‘set and setting’ framework.
Methods
Through an extensive literature review, we demonstrate the likely importance of the body in wellbeing, peak performance and peak experiences.
Results
Comprehensive multidisciplinary research, particularly focusing on various biomarkers, is needed to elucidate the potential role of bodyset in the psychedelic experience and therapy outcomes, and to guide future treatment approaches for mental health disorders.
Conclusion
Our exploration of the bodyset concept emphasizes the importance of considering not only psychological and environmental factors (mindset & setting), but also the physical state of the body in preparation for psychedelic experiences and psychedelic therapy. This holistic perspective may enhance our comprehension of their effects, therapeutic potential and inform the application of other treatment modalities, such as breathwork, in mental health care.
... The electrodes are placed following a standard "10-20" system, ensuring uniform coverage of all essential brain areas. The collected signals are amplified and recorded in an EEG machine, where they can be displayed as waves [22,24]. ...
This review examines the role of various bioelectrical signals in conjunction with artificial intelligence (AI) and analyzes how these signals are utilized in AI applications. The applications of electroencephalography (EEG), electroretinography (ERG), electromyography (EMG), electrooculography (EOG), and electrocardiography (ECG) in diagnostic and therapeutic systems are focused on. Signal processing techniques are discussed, and relevant studies that have utilized these signals in various clinical and research settings are highlighted. Advances in signal processing and classification methodologies powered by AI have significantly improved accuracy and efficiency in medical analysis. The integration of AI algorithms with bioelectrical signal processing for real-time monitoring and diagnosis, particularly in personalized medicine, is emphasized. AI-driven approaches are shown to have the potential to enhance diagnostic precision and improve patient outcomes. However, further research is needed to optimize these models for diverse clinical environments and fully exploit the interaction between bioelectrical signals and AI technologies.
... Some areas were aggregated to achieve 56 ROIs, aiming for fewer than the 64 total channels. Functional connectivity matrices were computed between the 56 regional time series using the PLV method across six frequency bands: Delta (0.5-3 Hz), Theta (4-8 Hz), Alpha (8-13.5 Hz), Beta (14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and Gamma . Connectivity matrices were first obtained separately for the PD and CTR groups, then concatenated before being input into the k-means++ clustering algorithm. ...
Objective
Parkinson’s Disease (PD) is a neurological disorder characterized by impaired postural control (PC) and balance issues. To date, few studies have explored the relationship between brain activity and responses during specific tasks designed to challenge balance in individuals with PD. Our exploratory research employs an innovative paradigm to assess PC by integrating virtual reality (VR) and electroencephalography (EEG).
Approach
In the study, 20 individuals diagnosed with PD who self-reported postural instability participated in the BioVRSea paradigm. This paradigm tested their PC using visuomotor stimuli and collected EEG signals to assess brain responses throughout the experiment. The results of the Parkinson’s group were compared with those of 22 age-matched healthy controls (CTR). From functional connectivity between brain regions, we employed novel techniques that use clustering algorithms to identify brain network states (BNSs). These BNSs define brain dynamics and can be compared with resting-state networks (RSNs) to further explore and identify neural alterations in individuals with PD.
Main Results
Six distinct BNSs were identified, with the dorsal attention network (DAN) dominant in five states. A significant reduction in the occurrence of BNS2 (p=0.005) was observed in PD patients during the PRE movement and visuomotor (MOV) phases compared to CTR. This reduced occurrence of BNS2 suggests impaired visuomotor integration in PD patients during PC tasks. DAN dominance highlights its crucial role in maintaining attentional control during the task.
Significance
The findings of this study highlight the potential of using brain dynamics as a biomarker of neural dysfunction in PD, especially during specific PC tasks. Altered BNSs, particularly in networks associated with attention and sensorimotor integration, reveal key neural deficits related to PD.
... Brain EEG EEG measures voltage oscillations that fall into five frequency bands: delta (1-4 Hz), theta (4-8 Hz), alpha (8)(9)(10)(11)(12), beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25), and gamma (>25 Hz). It records electrical activity from brain neurons along the scalp [36,37]. ...
Drowsiness and stress greatly influence worker health and productivity and workplace safety. Conflict between workplace expectations and employee control results in stress, which causes mental and physical reactions that affect performance and raise the risk of accidents at work. A common precursor to inadvertent drowsiness increases workplace risks and costs due to lost productivity and accidents. Developments in the interdisciplinary subject of neuroergonomics enable the creation of novel systems to track and minimize these issues. This work introduces prototype testing to demonstrate the system’s ability to detect stress and drowsiness. Along with other indicators such as body temperature, heart rate (HR), and SpO2 levels, the system incorporates electroencephalography (EEG) and heart rate variability (HRV). By analyzing these biosignals, the system detects stress and drowsiness in real time, providing alerts to both users and the supervisor’s BI dashboard. The design is flexible, offering two wearable forms: a headband and an armband. Prototype testing demonstrates the system’s ability to detect stress and drowsiness effectively, paving the way for safer and more productive workplace environments.
... It is used in clinical diagnoses to analyze the frequency and spectrum of brain waves, focusing on changes in recorded signals (Niedermeyer & da Silva, 2005). EEG waves are classified into delta, theta, alpha, beta, and gamma waves, with delta having the largest amplitude and lowest frequency (Niedermeyer, 2017). Various factors, such as eye movements and muscle activity, can create artifacts in EEG signals, affecting data interpretation. ...
Introduction and Objective: Research in design studies with a focus on stress indicates that stress is a persistent factor in the design profession and process. However, the nature, causes, and impacts of this stress have not been comprehensively and transparently examined. Despite efforts to clarify this issue, significant ambiguities remain, as no cohesive and systematic study has addressed the topic, and the fragmented nature of related discussions is evident. Recognizing stress in design as a prevalent and impactful phenomenon is crucial, as inadequate management can lead to acute or chronic stress, ultimately negatively affecting designers’ mental health, professional performance, and the quality of design outcomes. Accordingly, the present study aims to investigate stress arising from the design thinking process through a meta-synthesis approach.
Methodology: This study employs a systematic review based on the PRISMA protocol, scientometric analysis using the VOSviewer tool, grounded theory, and qualitative content analysis through coding to demystify the concept of design-related stress and propose a comprehensive framework for the ontology of design stress. Semi-structured clinical interviews (SCID-5/DSM-5) and the PANAS questionnaire were used to evaluate designers’ mental health, while EEG tools, alongside the PSS and PAQ questionnaires, measured stress arising from the design thinking process. The MetaCogno tool was utilized to capture designers’ emotional and cognitive experiences during the four main stages of the design process (problem analysis, ideation, idea evaluation and selection, and implementation of the final idea). Additionally, the CSI questionnaire assessed designers’ coping strategies in response to perceived stress levels. Stress pattern analysis was conducted using machine learning models, including Random Forest and SVM. Variables such as time constraints and handedness were also examined.
Findings and Contributions: After identifying appropriate methods for measuring design stress, the results categorized stress into three domains: cognitive design stress, neuropsychological design stress, and physiological design stress. Based on these categories, the novel MetaCogno approach was developed. The findings indicate that a unique type of stress, inherent to and arising from the design process itself, exists and introduces a new research domain termed “design stress.” All findings were structured into five ontology categories. Furthermore, the analysis revealed varying levels and intensities of stress across different stages of the design thinking process, impacting designers’ performance differently. Most designers experienced moderate stress levels, with continuous changes in mental states contributing to their adaptability. The ideation stage was
identified as the most stress-inducing phase, where designers reported the highest number of negative experiences, while the implementation stage showed reduced stress and improved performance. Moderate time constraints helped mitigate excessive stress and enhance focus, whereas the absence of time constraints led to fatigue and performance decline. Left-handed designers demonstrated better performance, greater mental stability, and lower stress levels compared to right-handed designers.
... The research suggests several neuroimaging approaches for occupational mental health assessment. The EEG signal is an electrical signal obtained on the scalp created by huge areas of coordinated brain activity called synchronization (groups of neurons firing simultaneously) (Niedermeyer and DA Silva 2005). EEG signals measure brain activity based on development and mental state. ...
Contemporary neuroscience scientists are interested in dyslexia, a complicated brain neurodevelopmental disorder. This condition causes slow and imprecise word comprehension in 5%-17% of the global population across languages and cultures. People with dyslexia often discuss mental health. On the scalp, the EEG signal shows coordinated neural activity that synchronizes. The EEG signal accurately captures these cerebral activity fluctuations due to evolution and mental state. Using statistical approaches, this study will determine if EEG waves indicate sickness. For this, three measures are suggested. The first metric, power spectral density, shows signal frequency and power distribution. The second metric assesses the model's uncertainty or randomness, conveying signal information, using entropy. The third metric, the Kolmogorov-Smirnov Test, uses entropy-based measurements to identify distributions based on Kolmogorov complexity. Applying these measures to the overall EEG signal of the twenty students under study separated the seven students' information from the other thirteen.
... Conversely, the IA-M and Post-M measurements showed an increase in alpha, beta, and theta waves. The increase in alpha and theta waves supports the theme of a relaxed state in the brain following the intervention, as alpha and theta waves predominate in states of relaxation and mental inactivity [23,24]. While beta waves are traditionally associated with states of attention [25], Ossebaard [46] has shown that stimulation of beta waves was effective in reducing emotional exhaustion and anxiety. ...
Background: High-velocity, low-amplitude (HVLA) manipulation is a common manual therapy technique used for treating pain and musculoskeletal dysfunction. An audible manipulation sound is commonly experienced by patients who undergo HVLA manipulation; however, there is little known about the effects and clinical relevance of the audible manipulation sound on cortical output and the autonomic nervous system. This study aimed to identify the immediate impact of the audible manipulation sound on brainwave activity and pupil diameter in asymptomatic subjects following an HVLA cervical manipulation. Methods: 40 subjects completed this quasi-experimental repeated measure study design. Subjects were connected to electroencephalography and pupillometry simultaneously, and an HVLA cervical distraction manipulation was performed. The testing environment was controlled to optimize brainwave and pupillometry data acquisition. Pre-manipulation, immediately after manipulation, and post-manipulation data were collected. The presence of an audible manipulation sound was noted. Results: Twenty subjects experienced an audible manipulation sound. Brainwave activity changes were significant (p < 0.05) in both the audible manipulation sound and non-manipulation sound groups. Pupil diameter changes (p < 0.05) occurred in both eyes of the non-manipulation sound group and in the left eye of the audible-manipulation sound group. Brainwave activity patterns were similar in both groups. Conclusions: The presence of an audible manipulation sound is not required to produce central nervous system changes following an HVLA cervical manipulation; however, the audible manipulation sound does prolong the effects of brainwave activity, indicating a prolonged relaxation effect.
... Le premier é ectroencépha ogramme (EEG) a en effet été mesuré en 1924 par e psychiatre a emand Hans Berger [19]. 'EEG est uti isé depuis ce temps notamment pour effectuer des diagnostics médicaux dans e domaine c inique, ou pour comprendre e fonctionnement cérébra en neurosciences [34]. ...
... It is conceivable to build real-time, bidirectional, functional interfaces between artificial devices and living brain tissue, according to recent studies. It is plausible to anticipate that additional investigation into brain-machine interfaces will result in the creation of a novel range of neuro prosthetic apparatuses designed to restore motor abilities in individuals with severe paralysis [1][2][3][4][5][6][7][8][9] Eight distinct statistical variables were derived from each participant's twenty minutes of EEG data recording in order to identify clench and attention signals [9]. The authors suggested a strong learning control that is predicated on the deep auto encoder's unsupervised learning method [10]. ...
... The electroencephalogram (EEG) provides a noninvasive picture of brain electrical activity with a high temporal resolution of the order of milliseconds. As a clinical tool, it has been used successfully in a wide range of contexts, including characterizing sleep disorders, charting the effects of focal abnormalities and epilepsy, and as an aid in the diagnosis of dementia (Niedermeyer and Lopes da Silva, 1982). Applications to other disorders, such as attention-deficit hyperactivity disorder (ADHD), post-traumatic stress disorder (PTSD), and depression, have also been investigated. ...
The extent of intra-individual and inter-individual variability is an important factor in determining the statistical, and hence possibly clinical, significance of observed differences in the EEG. This study investigates the changes in classical quantitative EEG (qEEG) measures, as well as of parameters obtained by fitting frequency spectra to a continuum model of brain electrical activity. These parameters may have extra variability due to model selection and fitting. Besides estimating levels of intra-individual and inter-individual variability, we determine approximate time scales for change in qEEG measures and model parameters. This provides an estimate of the recording length needed to capture a given percentage of the total intra-individual variability. Also, if more precise time scales can be obtained in future, these may aid the characterization of physiological processes underlying various EEG measures. Heterogeneity of the subject group was constrained by testing only healthy males in a narrow age range (mean = 22.3 years, sd = 2.7). Resting eyes-closed EEGs of 32 subjects were recorded at weekly intervals over an approximately six-week period. Of these, 13 subjects had follow-up recordings spanning up to a year. QEEG measures, computed from Cz spectra, were powers in five frequency bands, alpha peak frequency, and spectral entropy. Of these, theta, alpha, and beta band powers were most reproducible. Of nine model parameters obtained by fitting model predictions to experiment, the most reproducible ones quantified the total power and the time delay between cortex and thalamus. About 95% of the maximum change in spectral parameters was reached within minutes of recording time, implying that repeat recordings are not necessary to capture the bulk of the variability in EEG spectra likely to occur in the resting eyes-closed state on the scale of a year.
... Biomedical signal processing [115] is a domain where researchers use recorded electrical activity from the human body to solve problems in bioinformatics. Various data from EEG [152], electrocorticography (ECoG) [153], electrocardiography (ECG) [154], electromyography (EMG) [155], and electrooculography (EOG) [156,157] have been used, with most studies focusing on EEG activity so far. Because recorded signals are usually noisy and include many artifacts, raw signals are often decomposed into wavelet or frequency components before they are used as input in deep learning algorithms. ...
In the era of big data, transformation of biomedical big data into valuable knowledge has been one of the most important challenges in bioinformatics. Deep learning has advanced rapidly since the early 2000s and now demonstrates state-of-the-art performance in various fields. Accordingly, application of deep learning in bioinformatics to gain insight from data has been emphasized in both academia and industry. Here, we review deep learning in bioinformatics, presenting examples of current research. To provide a useful and comprehensive perspective, we categorize research both by the bioinformatics domain (i.e., omics, biomedical imaging, biomedical signal processing) and deep learning architecture (i.e., deep neural networks, convolutional neural networks, recurrent neural networks, emergent architectures) and present brief descriptions of each study. Additionally, we discuss theoretical and practical issues of deep learning in bioinformatics and suggest future research directions. We believe that this review will provide valuable insights and serve as a starting point for researchers to apply deep learning approaches in their bioinformatics studies.
... Such large imaging studies include the ADNI (Alzheimer's Disease Neuroimaging Initiative), the longitudinal magnetic resonance imaging (MRI) study of schizophrenia, autism, and attention deficit hyperactivity disorder (ADHD), the NIH human connectome project, among many others. Neuroimaging studies usually collect structural, neurochemical, and functional images over both time and space [15,16,33]. These structural, neurochemical, and functional imaging modalities include computed axial tomography (CT), diffusion tensor imaging (DTI), functional magnetic resonance imaging (fMRI), magnetic resonance imaging (MRI), magnetic resonance spectroscopy (MRS), positron emission tomography (PET), single photon emission tomography (SPECT), electroencephalography (EEG), and magnetoencephalography (MEG), among many others. ...
We propose a prediction procedure for the functional linear quantile regression model by using partial quantile covariance techniques and develop a simple partial quantile regression (SIMPQR) algorithm to efficiently extract partial quantile regression (PQR) basis for estimating functional coefficients. We further extend our partial quantile covariance techniques to functional composite quantile regression (CQR) defining partial composite quantile covariance. There are three major contributions. (1) We define partial quantile covariance between two scalar variables through linear quantile regression. We compute PQR basis by sequentially maximizing the partial quantile covariance between the response and projections of functional covariates. (2) In order to efficiently extract PQR basis, we develop a SIMPQR algorithm analogous to simple partial least squares (SIMPLS). (3) Under the homoscedasticity assumption, we extend our techniques to partial composite quantile covariance and use it to find the partial composite quantile regression (PCQR) basis. The SIMPQR algorithm is then modified to obtain the SIMPCQR algorithm. Two simulation studies show the superiority of our proposed methods. Two real data from ADHD-200 sample and ADNI are analyzed using our proposed methods.
... Electroencephalography (EEG) is a highly effective and non-invasive technique used to monitor and analyze sleep stages by detecting variations in brain electrical activity [12]. By placing electrodes on the scalp, EEG sensors capture the electrical signals produced by the brain's neurons [13]. ...
Sleep is an essential part of human life, and sleep quality is a crucial indicator of a person’s health. This paper introduces a system using a Brain-Computer Interface and Deep Learning Network for real-time classification of light sleep and deep sleep. By collecting, uploading, preprocessing, and classifying sleep EEG in real-time, the system provides a quantitative display of the subjects’ sleep quality. Additionally, we conducted sleep experiments on the same subject under different environments and interventions. Based on our deep learning model, we measured the proportion of deep sleep for each experimental group. Using this system, we analyzed the specific effects of various environments and interventions on sleep states, providing concrete data to support whether these factors improve sleep quality.
... The EEG signal has potential to acquire enormous brain signals from the scalp without any surgical risks (Wang et al., 2016). It measures and records electrical signals generated by active neurons in the brain (Niedermeyer & da Silva, 2005). The benefits of using EEG are its great temporal resolution, accessibility, and flexibility for a wide range of medical and scientific applications (Lotte et al., 2018). ...
Motor imagery (MI)-based brain computer interfaces (BCIs) frequently use convolutional neural networks (CNNs) to analyse electroencephalography (EEG) signals. In this study, we proposed a novel methodology that includes an innovative preprocessing step and a new model for MI EEG classification. In the preprocessing, we use common average reference (CAR) filtering and Laplace filtering of EEG signals. The CAR filter eliminates the overall noise and Laplace filter removes the neighbouring electrode noise. Additionally, a sliding window method is used to increases the number of small-time segments which prevents overfitting. Next, the time segments are converted into spectrograms using the short-time Fourier transform (STFT). Further, the concatenated spectrogram images of mu and beta bands are processed using a CNN model with self-attention. The proposed model uses both local and global information to effectively extract features The EEG signals obtained from BCI competition IV dataset-2a are divided into 80:20 ratio for training and testing. Moreover, the ablation study highlights the importance of the combination of CAR and Laplace filters. The classification results obtained using proposed methodology shows advancement as compared to state-of-the-art methods. Finally, the proposed CNN model learning and feature distribution are visualized with the gradient weight class activation map.
... Electroencephalography signals, reflecting brain electrical activity, are instrumental in diagnosing and monitoring various neurological conditions [1]. The advent of telemedicine has expanded the reach of EEG-based healthcare, enabling remote monitoring and diagnosis [2]. ...
... Electroencephalography (EEG) measures multi-channel electric brain activity from the human scalp (Niedermeyer & da Silva, 2005) and can reveal cognitive processes (Pfurtscheller & Da Silva, 1999), emotion states (Suhaimi et al., 2020), and health status (Alotaiby et al., 2014). Neurotechnology and brain-computer interfaces (BCI) aim to extract patterns from the EEG activity that can be utilized for various applications, including rehabilitation and communication (Wolpaw et al., 2002). ...
The non-stationary nature of electroencephalography (EEG) introduces distribution shifts across domains (e.g., days and subjects), posing a significant challenge to EEG-based neurotechnology generalization. Without labeled calibration data for target domains, the problem is a source-free unsupervised domain adaptation (SFUDA) problem. For scenarios with constant label distribution, Riemannian geometry-aware statistical alignment frameworks on the symmetric positive definite (SPD) manifold are considered state-of-the-art. However, many practical scenarios, including EEG-based sleep staging, exhibit label shifts. Here, we propose a geometric deep learning framework for SFUDA problems under specific distribution shifts, including label shifts. We introduce a novel, realistic generative model and show that prior Riemannian statistical alignment methods on the SPD manifold can compensate for specific marginal and conditional distribution shifts but hurt generalization under label shifts. As a remedy, we propose a parameter-efficient manifold optimization strategy termed SPDIM. SPDIM uses the information maximization principle to learn a single SPD-manifold-constrained parameter per target domain. In simulations, we demonstrate that SPDIM can compensate for the shifts under our generative model. Moreover, using public EEG-based brain-computer interface and sleep staging datasets, we show that SPDIM outperforms prior approaches.
... EEG is a dynamic, non-invasive, and relatively inexpensive technique used to monitor brain electrical activity in micro-voltages [5]. EEG signals are biopotentials formed from brain activities [6], [7]. Despite their complexity and susceptibility to noise from muscle movements and eye blinking, EEG signals [20] designed an EEG-based authentication system using one-class and multi-class classifiers, specifically isolation forest and local outlier factor, and identified key EEG channels and brainwaves, comparing their contributions to traditional dimensionality reduction techniques like PCA and χ2 tests. ...
This study presents a novel approach to electroencephalography (EEG) biometric authentication using eye blink artifacts. Unlike traditional methods that rely on imagination and mental tasks, which are susceptible to emotional and physical variations, this approach leverages the consistent effects of eye blinks on brainwaves for authentication. Brainwaves were recorded using the NeuroSky Mindwave Mobile 2 headset, and eye blinks were extracted via NeuroSky’s blink detection algorithm. An authentication algorithm was developed based on blink strength, time, and frequency. The proposed method demonstrated high performance with an accuracy (ACC) of 97%, a false acceptance rate (FAR) of 5%, and a false rejection rate (FRR) of 1%. This study also explored the impact of emotions and physical exercise on the authentication process, confirming the method's robustness under varying conditions. These findings suggest that eye blink artifacts offer a reliable and practical biometric trait for EEG-based authentication systems, providing a secure alternative to traditional biometric methods. The substantial contribution of this research lies in demonstrating the superior stability and usability of eye blink-based EEG authentication under diverse conditions, compared to existing EEG authentication methods that often require mental tasks or multi-channel recordings.
The primary goal of this study is to provide students with practical experience in measuring and analyzing electroencephalogram (EEG) signals. This involves understanding the effects of visual stimulation on alpha wave activity and managing interference signals to ensure accurate recordings. The human brain's cortex contains numerous neurons that can synchronize, resulting in rhythmic behaviors detectable as electrical signals on the scalp. These signals, known as EEGs, are crucial for studying brain activity and diagnosing neurological conditions. Different states of alertness and external stimuli affect these signals, which can be categorized into delta, theta, alpha, and beta waves based on their frequency.
This paper introduces a novel software/firmware layer for electroencephalography (EEG) and electrocardiography (EKG) devices, designed to enhance diagnostic accuracy by reconstructing dynamic waveforms from discrete bioelectrical signals. The system employs spline interpolation, Savitzky-Golay smoothing, Fast Fourier Transform (FFT), and dynamic band-pass filtering to capture subtle temporal and spectral variations, enabling early detection of anomalies such as epileptic seizures and cardiac arrhythmias. Adaptive Eco and Hyper modes optimize energy efficiency and precision, ensuring compatibility with existing clinical workflows without hardware modifications. Preliminary simulations demonstrate improved sensitivity, with ongoing validation planned using clinical datasets.
Article type: Research Article The aim of this study is to employ quantitative electroencephalography (qEEG) to map the brain of cognitive empathy in neuroleadership. The research method is predicated on the interpretation of two distinct phases of the brain map of participants from the community of managers who have a minimum of 5-10 years of experience leading groups and a maximum of 20-30 years of experience. In order to evaluate the capabilities of neuroleadership, the samples were analyzed and interpreted using the 2018 version of the NeuroGuide software. The disparities between the two phases in the participants were also analyzed. In the initial phase, only the waves were recorded. The second phase, which involved cognitive exercises and a four-month interval, was dedicated to the investigation of the empathy component, particularly in the brain cortex. This phase was conducted using the scarf model, clinical interview, two-stage interpretation at the University of Tehran counseling center, and cognitive verification. In 2022-2023, it was conducted on 12 participants as a statistical sample. Positive empathy predicted an increase in the activation of the left dorsal (frontal pole) (P<0.05). The activity of participants has been observed to alter in order to perform a pleasurable task, consistent with their positive emotions and empathy (both positive and negative). With transform (FFT) signal processing and analysis have been used to calculate Discrete Fourier Transform (DFT) as well as its inverse fully known as Inverse Discrete Fourier Transform (IDFT). According to the interpretation of brain maps, the results of this study indicate a direct correlation between leadership, functions, and semantic processing in empathy.
Mental and neurological disorders significantly impact global health. This systematic review examines the use of artificial intelligence (AI) techniques to automatically detect these conditions using electroencephalography (EEG) signals. Guided by Preferred Reporting Items for Systematic Reviews and Meta‐Analysis (PRISMA), we reviewed 74 carefully selected studies published between 2013 and August 2024 that used machine learning (ML), deep learning (DL), or both of these two methods to detect neurological and mental health disorders automatically using EEG signals. The most common and most prevalent neurological and mental health disorder types were sourced from major databases, including Scopus, Web of Science, Science Direct, PubMed, and IEEE Xplore. Epilepsy, depression, and Alzheimer's disease are the most studied conditions that meet our evaluation criteria, 32, 12, and 10 studies were identified on these topics, respectively. Conversely, the number of studies meeting our criteria regarding stress, schizophrenia, Parkinson's disease, and autism spectrum disorders was relatively more average: 6, 4, 3, and 3, respectively. The diseases that least met our evaluation conditions were one study each of seizure, stroke, anxiety diseases, and one study examining Alzheimer's disease and epilepsy together. Support Vector Machines (SVM) were most widely used in ML methods, while Convolutional Neural Networks (CNNs) dominated DL approaches. DL methods generally outperformed traditional ML, as they yielded higher performance using huge EEG data. We observed that the complex decision process during feature extraction from EEG signals in ML‐based models significantly impacted results, while DL‐based models handled this more efficiently. AI‐based EEG analysis shows promise for automated detection of neurological and mental health conditions. Future research should focus on multi‐disease studies, standardizing datasets, improving model interpretability, and developing clinical decision support systems to assist in the diagnosis and treatment of these disorders.
Wearable noninvasive brain–computer interface (BCI) technologies, such as electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), have experienced significant progress since their inception. However, these technologies have not achieved revolutionary advancements, largely because of their inherently low signal-to-noise ratio and limited penetration depth. In recent years, the application of quantum-theory-based optically pumped (OP) technologies, particularly optical pumped magnetometers (OPMs) for magnetoencephalography (MEG) and photoacoustic imaging (PAI) utilizing OP pulsed laser sources, has opened new avenues for development in noninvasive BCIs. These advanced technologies have garnered considerable attention owing to their high sensitivity in tracking neural activity and detecting blood oxygen saturation. This paper represents the first attempt to discuss and compare technologies grounded in OP theory by examining the technical advantages of OPM-MEG and PAI over traditional EEG and fNIRS. Furthermore, the paper investigates the theoretical and structural feasibility of hardware reuse in OPM-MEG and PAI applications.
Certain gastrointestinal disorders could present with a diverse number of neurological signs and symptoms resembling different neurological conditions. In a distinct number of patients, the first sign of underlying gastrointestinal disease could be a neurological sign or symptom indicating a neurological problem. There are some specific neurological symptoms which are frequently seen with distinct gastrointestinal disorders, but in many other cases no specific sign or symptom could lead a neurologist to a suspicion on a gastrointestinal disorder which is most frequently diagnosed only after all neurological causes of patients′ symptoms have been excluded. On the other hand, gastrointestinal disorders of unknown cause could sometimes resemble certain neurological disorders. Hence, it is important for both gastroenterologists and neurologists to be familiar with conditions which could present with symptoms characteristic of both systems. In this chapter, we will present the most frequent conditions in which symptoms overlap between neurology and gastroenterology.
In the field of medical science, particularly in neuroscience, recent studies have focused heavily on combining artificial intelligence with electroencephalography (EEG) for brain–computer interfaces (BCI). This area has become a crucial domain for research because of its potential to understand brain activity and develop new technologies to interpret brain signals for various applications. The goal of Brain-Computer Interfaces (BCIs) is to help people with speaking disabilities communicate. BCIs connect the brain to a computer, allowing individuals to control devices or communicate based on their thoughts. Brain signals are usually measured using an EEG. EEG signals constantly change and are unique to each person, varying in frequency. This study aimed to explore the potential of electroencephalogram (EEG) signals in classifying imagined speech data, with a focus on understanding the brain's response to speech-related activities and its application in BCI. This study highlights the importance of feature extraction techniques, including time-domain, frequency-domain, and time-frequency domain analyses, in enhancing the classification of EEG-based imagined speech data and covering EEG signal processing and classification, including data acquisition, pre-processing, feature extraction, and classification. Linear classifiers, such as support vector machines and logistic regression, are employed alongside neural networks, particularly convolutional neural networks (CNNs) and artificial neural networks (ANNs), to analyze and classify EEG data associated with imagined speech and applications of EEG. Research indicates that EEG data used for analyzing brain activity are complex and can be gathered via different techniques using different devices. Multiple steps, such as preprocessing, feature extraction, and classification, may be necessary based on the signal collection method and study objectives.
This Special Issue aims to capture current theoretical and methodological developments in the field of metareasoning, which is concerned with the metacognitive processes that monitor and control our ongoing thinking and reasoning [...]
Electroencephalography (EEG) has revolutionized our understanding of brain activity, providing a non-invasive window into the complex workings of the nervous system. While human EEG studies have long been the focus of neuroscience, exploring EEG patterns in non-human species unveils an extraordinary diversity of neural adaptations shaped by evolution. From dolphins exhibiting unihemispheric sleep to cephalopods with distributed neural systems and birds consolidating memories during sleep, these patterns reflect the unique ecological pressures and behavioral needs of different species. Studying EEGs across the animal kingdom reveals both universal principles of neural activity and remarkable adaptations, offering profound insights into the evolution of cognition, sleep, and sensory processing. By examining how species like bats, elephants, and octopuses utilize their brains to navigate complex environments, researchers uncover new perspectives on brain function that extend beyond the human mind. This comparative analysis not only enhances our understanding of brain evolution but also inspires innovations in fields such as artificial intelligence, robotics, and bio-inspired technology.
Keywords: EEG, non-human species, comparative neuroscience, brain evolution, unihemispheric sleep, REM-like states, neural oscillations, cephalopods, dolphins, sensory processing, artificial intelligence, memory consolidation, robotics, bio-inspired technology.
With the increasing research of deep learning in the EEG field, it becomes more and more important to fully extract the characteristics of EEG signals. Traditional EEG signal classification prediction neither considers the topological structure between the electrodes of the signal collection device nor the data structure of the Euclidean space to accurately reflect the interaction between signals. Graph neural networks can effectively extract features of non-Euclidean spatial data. Therefore, this paper proposes a feature selection method for epilepsy EEG classification based on graph convolutional neural networks (GCNs) and long short-term memory (LSTM) cells. While enriching the input of LSTM, it also makes full use of the information hidden in the EEG signals. In the automatic detection of epileptic seizures based on neural networks, due to the strong non-stationarity and large background noise of the EEG signal, the analysis and processing of the EEG signal has always been a challenging research. Therefore, experiments were conducted using the preprocessed Boston Children’s Hospital epilepsy EEG dataset, and input it into the GCN-LSTM model for deep feature extraction. The GCN network built by the graph convolution layer learns spatial features, then LSTM extracts sequence information, and the final prediction is performed by fully connected and softmax layers. The introduced method has been experimentally proven to be effective in improving the accuracy of epileptic EEG seizure detection. Experimental results show that the average accuracy of binary classification on the CHB-MIT dataset is 99.39%, and the average accuracy of ternary classification is 98.69%.
A eletroencefalografia (EEG) é uma técnica neurofisiológica não invasiva que mede a atividade elétrica do cérebro através de elétrodos no couro cabeludo, sendo amplamente empregada na investigação científica e no diagnóstico clínico. Este artigo descreve a metodologia para a utilização do EEG, abordando os seus principais componentes, como elétrodos, amplificadores e software de análise, bem como os protocolos de boas práticas para a recolha e processamento de dados. A técnica oferece alta resolução temporal, crucial para estudos sobre funções cognitivas e respostas a estímulos. Apesar das suas limitações, como baixa resolução espacial e sensibilidade a artefactos, o EEG destaca-se pela sua acessibilidade e aplicabilidade em estudos clínicos e experimentais. Exemplos da sua aplicação incluem a análise de potenciais relacionados com eventos (ERPs) e a investigação de conectividade funcional entre áreas corticais, contribuindo para o entendimento de redes neurais e a sua relação com processos cognitivos e emocionais. As vantagens e desafios associados ao EEG são discutidos, destacando a sua relevância como ferramenta essencial nas neurociências.
Як відомо, візуалізація інформації значно спрощує її сприйняття, особливо
якщо це правила граматики з іноземної мови. Саме метод наочності є одним з
найефективніших при вивченні нових тем. З розвитком технологій для людини
відкрилась безліч можливостей для спрощення навчання та полегшеної обробки
нового матеріалу. Деякі з них ми пропонуємо для детального розгляду.
The pursuit of understanding the brain’s complex mechanisms is at the forefront of neuroscience, especially in developing early diagnostic tools for neurological diseases. Advances in neural fields, brain dynamics, and the integration of applied physics have opened new possibilities for identifying biomarkers indicative of neurological conditions such as Alzheimer’s disease, schizophrenia, and ADHD. Each of these fields contributes uniquely: neural fields offer a spatially distributed model of brain activity, brain dynamics provide insights into temporal patterns and oscillations, and applied surface physics enhances the precision of neural interface technologies and imaging. Combined, they form a multidisciplinary foundation that allows researchers to study the brain’s intricate structures and functions with unprecedented clarity, revealing potential pathways for diagnosing and treating neurological disorders.
The pursuit of understanding the brain's complex mechanisms is at the forefront of neuroscience, especially in developing early diagnostic tools for neurological diseases. Advances in neural fields, brain dynamics, and the integration of applied physics have opened new possibilities for identifying biomarkers indicative of neurological conditions such as Alzheimer's disease, schizophrenia, and ADHD. Each of these fields contributes uniquely: neural fields offer a spatially distributed model of brain activity, brain dynamics provide insights into temporal patterns and oscillations, and applied surface physics enhances the precision of neural interface technologies and imaging. Combined, they form a multidisciplinary foundation that allows researchers to study the brain's intricate structures and functions with unprecedented clarity, revealing potential pathways for diagnosing and treating neurological disorders.
In the rat it was demonstrated that action currents appear in all cortical fields studied (motor, common sensory, visual, and auditory) as a direct result of both ipsilateral and contralateral Achilles reflex stimulation. The mean latency of such action current appearances (time elapsing between the striking of the Achilles tendon and the instant of the action current incipience in the brain) was 0.0093 second. The mean Achilles reflex response latency (time elapsing between the striking of the Achilles tendon and the arrival of action currents in the gastrocnemius muscle) was 0.0067 second. There were no consistent differences either in the latencies for the different cortical fields or in the latencies for the same field upon ipsilateral and contralateral stimulation. The rate of conduction of the electrical changes was significantly faster over the path to the muscle (19.3 meters per second) than over the path to the brain (16.1 meters per second). It is conceived that every part of the cerebral cortex is involved in reflex activity and that the highest neurological levels combine with the lowest neurological levels to form a functional unity. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
The study of the early history of electroencephalography can yield fascinating insights and surprises. A revisit to the work of Mario Gozzano (1898–1986) has proved to be particularly stimulating. His EEG study of 1935 is a classic and should be resurrected from the graveyard of history.
Gozzano was an eminent clinical neurologist-epileptologist and chairman of the neurological-psychiatric university departments in Cagliari, Pisa, Bologna and, from 1951 to his retirement, in Rome. He quickly recognized the significance of EEG and produced his major experimental EEG work in the wake of a stay at the Berlin-Buch Brain Institute. His prolonged corticograms of various regions in the rabbit demonstrated striking differences between various cortical areas. Topical cortical strychnine produced spikes (a barely known phenomenon at that time) and the evolution from interictal to ictal spiking. Spikes induced by visual stimuli may be regarded as precursors of evoked potentials.
While Hans Berger was a holist (“the brain working as a whole”), Gozzano (influenced by Vogt and Kornmueller) provided EEG support for the localizationists.
1.Stimuli in any modality evoke responses over wide regions of frontal non-specific human cortex.2.When identical stimuli are monotonously repeated these responses diminish irregularly and finally disappear.3.When stimuli are presented frequently in association, the responses to the first or conditional stimuli are amplified while those to the second or indicative stimuli are attenuated.4.When a subject is instructed to perform a relevant operant action to the second “imperative” stimulus the primary response to the conditional stimulus is followed by a prolonged negative wave which submerges the negative component of the imperative response.5.The negative wave linking the conditional and imperative responses is described as an Expectancy Wave (E-wave) because it reflects very accurately the attitude of the subject to the stimulus association and his intention to act on it.6.Comparison of intra-cranial and scalp records suggests that the E-wave arises from depolarisation of a small proportion of the apical dendrites in the frontal and premotor cortex.7.Development of the E-wave is accompanied by economical abbreviation of the motor reaction time to the imperative stimuli by synchronisation and restriction of the efferent motor volleys.8.When the imperative stimuli are withdrawn (extinction) without warning the E-wave subsides slowly over about 50 trials in normal adults. However, when a previous verbal warning is given the E-wave disappears at once.9.When the significance of the association between conditional and imperative stimuli is diluted by presenting a proportion of unreinforced conditional stimuli (equivocation) the E-wave is diminished accordingly and in normal adults vanishes when the probability of association falls to about 0.5.10.Stimuli involving no energy transfer to the subject but with a high information content evoke E-waves as long as the subject considers the signals interesting and important, whether they are isolated, imperative or conditional.11.The E-wave seems to indicate the subjective significance assigned by a particular person to the signal association or “Gestalt” used for the experiment. The significance thus determined includes the need for recognition or decision, and involves social as well as physiological influences.
There is need nowadays to re-emphasize the capabilities of electroencephalography: a method representing the extremely important function/dysfunction-orientation in neurological thinking and practice. Valuable and relevant messages to the clinician naturally require solid EEG training and the resulting expertise.
The idea that valuable EEG information is limited to the field of epileptology is erroneous. A plethora of clinically relevant messages can be derived from the EEG in nonepileptic conditions and, above all, in metabolic (and so called “mixed”) encephalopathies where neuroimaging has almost nothing to offer.
The discussion of EEG and epileptology only skirts pediatric conditions (and most of the epileptic syndromes). It is shown that EEG reading in epileptology is a lot more than simply “hunting spikes.” A strong plea is being made against the presently fashionable overuse of the term “non-convulsive status epilepticus.”
Continuing neglect of functional/dysfunctional orientation can seriously endanger the entire field of Neurology.
The electrical activity of the cortex and subcortex were studied in 22 patients with epilepsy by means of scalp electrodes and a multi-electrode needle which was placed in the depths of the brain with the aid of a stereotaxic instrument. The instrument was designed for man and coordinate measurements were obtained by studies on brains fixed in situ. In the present operative series the position of the needle was checked by pneumoencephalography at the end of the recording. Analysis of records from the scalp and depths of the brain lead to the following conclusions: 1. The cortex and sub-cortex in epileptic patients display comparable normal and abnormal activity. 2. Like the cortex, however, various areas of the sub-cortex may show entirely independent abnormal activity. 3. During the induction of pentathol anesthesia 20 to 30 per second activity appeared first and remained most prominent in outer subcortical leads and showed least in leads from the central grey mass. 4. Isolated seizure discharges from the cortex are common, whereas they are rare in the subcortex. The fact that they do occur is important, because they may account for therapeutic failure in some cases where a cortical focus is ablated. 5. No case was found in which seizure discharges in or around the medial thalamus could be interpreted as initiating 3 per second wave-and-spike discharges of the petit mal type. 6. Primary and secondary cortical discharges have negative sign when referred to a relatively indifferent area. An incoming volley from a distant area is registered as a positive disturbance if referred to an indifferent region. 7. Electrical sign has localizing value in electroencephalography: Negativity indicates a local disturbance; positivity indicates a distant disturbance.
Action current studies of simultane-ously active disparate fields of the central nervous system of the rat
- L E Travis
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Uber das Elektrenkephalogramm des Menschen. 1st re-port Uber das Elektrenkephalogramm des Menschen. 4th re-port Uber das Elektrenkephalogramm des Menschen. 7th re-port Electrical study of the cerebral cortex as compared to the action potential of excised nerve Brain potentials during sleep
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