Kim Dremstrup

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• Head of Dep., M.Sc.-BME, PhD
• Head of Department at Aalborg University

p>Preprint submitted to IOP Journal of Neural Engineering Objective. Individuals with Amyotrophic lateral sclerosis (ALS) progressively lose muscle functionality and therefore experience both an increased need for assistive robot technologies and a reduced ability to control such robots. While these individuals may use high-performing control syst...

p>Preprint submitted to IOP Journal of Neural Engineering Objective. Individuals with Amyotrophic lateral sclerosis (ALS) progressively lose muscle functionality and therefore experience both an increased need for assistive robot technologies and a reduced ability to control such robots. While these individuals may use high-performing control syst...

p>Preprint submitted to IOP Journal of Neural Engineering Objective. Individuals with Amyotrophic lateral sclerosis (ALS) progressively lose muscle functionality and therefore experience both an increased need for assistive robot technologies and a reduced ability to control such robots. While these individuals may use high-performing control syst...

p>Individuals suffering from progressive neuromuscular diseases gradually lose all muscle control and therefore are forced to repeatedly adapt to new control interface technologies to maintain some level of independence. Accordingly, the ideal interface technology should adapt to the progression of paralysis. We propose an adaptive tongue-brain hyb...

p>Individuals suffering from progressive neuromuscular diseases gradually lose all muscle control and therefore are forced to repeatedly adapt to new control interface technologies to maintain some level of independence. Accordingly, the ideal interface technology should adapt to the progression of paralysis. We propose an adaptive tongue-brain hyb...

Brain-computer interface performance may be reduced over time, but adapting the classifier could reduce this problem. Error-related potentials (ErrPs) could label data for continuous adaptation. However, this has scarcely been investigated in populations with severe motor impairments. The aim of this study was to detect ErrPs from single-trial EEG...

Error-related potentials (ErrPs) have been proposed as a means for improving brain–computer interface (BCI) performance by either correcting an incorrect action performed by the BCI or label data for continuous adaptation of the BCI to improve the performance. The latter approach could be relevant within stroke rehabilitation where BCI calibration...

BACKGROUND More than 37 million people throughout the world are diagnosed with heart failure that is a growing burden on the health sector. Cardiac rehabilitation aims to improve patients’ recovery, functional capacity, psychosocial well-being, and health-related quality of life. However, cardiac rehabilitation programs have poor compliance and adh...

Background More than 37 million people worldwide have been diagnosed with heart failure, which is a growing burden on the health sector. Cardiac rehabilitation aims to improve patients’ recovery, functional capacity, psychosocial well-being, and health-related quality of life. However, cardiac rehabilitation programs have poor compliance and adhere...

Error-related potentials (ErrPs) have been proposed for designing adaptive brain-computer interfaces (BCIs). Therefore, ErrPs must be decoded. The aim of this study was to evaluate ErrP decoding using combinations of different feature types and classifiers in BCI paradigms involving motor execution (ME) and imagination (MI). Fifteen healthy subject...

Objective. Brain computer interfacing (BCI) is a promising method to control assistive systems for patients with severe disabilities. Recently, we have presented a novel BCI approach that combines an electrotactile menu and a brain switch, which allows the user to trigger many commands robustly and efficiently. However, the commands are timed to pe...

An assistive robotic manipulator (ARM) can provide independence and improve the quality of life for patients suffering from tetraplegia. However, to properly control such device to a satisfactory level without any motor functions requires a very high performing brain-computer interface (BCI). Steady-state visual evoked potentials (SSVEP) based BCI...

Brain-computer interfaces (BCIs) have been developed for several purposes in communication, control, and rehabilitation. To use the BCI efficiently, the system must be technically tuned, and the user must learn to operate it. In this study, we investigated if the user could be trained to improve the performance of online detection of movement-relat...

Brain-computer interfaces (BCIs) have been developed for several purposes in communication, control, and rehabilitation. To use the BCI efficiently, the system must be technically tuned, and the user must learn to operate it. In this study, we investigated if the user could be trained to improve the performance of online detection of movement-relat...

The detection of single trial movement intentions from EEG is paramount for brain-computer interfacing in neurorehabilitation. These movement intentions contain task-related information about the forthcoming movement and if this is decoded the neuro rehabilitation could potentially be optimized. The aim of this study was to classify single trial mo...

Over the past several years, our group has conceived a completely new technological approach toward BCIs aimed at reversing the maladaptive plasticity induced by musculoskeletal pain. The EEG activity patterns of participants with chronic pain (tennis elbow) were differentiated from those of healthy, age and sex matched controls during real-time mo...

Objective: In this study, we analyzed the influence of artificially imposed attention variations using the auditory oddball paradigm on the cortical activity associated to motor preparation/execution. Methods: EEG signals from Cz and its surrounding channels were recorded during three sets of ankle dorsiflexion movements. Each set was interspers...

In this study, we present a novel multi-class brain-computer interface (BCI) system for communication and control. In this system, the information processing is shared by the algorithm (computer) and the user (human). Specifically, an electro-tactile cycle was presented to the user, providing the choice (class) by delivering timely sensory input. T...

Brain-computer interfacing (BCI) has recently been applied as a rehabilitation approach for patients with motor disorders, such as stroke. In these closed-loop applications, a brain switch detects the motor intention from brain signals, e.g., scalp EEG, and triggers a neuroprosthetic device, either to deliver sensory feedback or to mimic real movem...

Objectives: Studies have shown decreases in N30 somatosensory evoked potential (SEP) peak amplitudes following spinal manipulation (SM) of dysfunctional segments in subclinical pain (SCP) populations. This study sought to verify these findings and to investigate underlying brain sources that may be responsible for such changes. Methods: Nineteen SC...

Brain-computer interfaces (BCIs) have the potential to improve functionality in chronic stoke patients when applied over a large number of sessions. Here, we evaluate the effect and the underlying mechanisms of three BCI training sessions in a double-blind-sham-controlled design. The applied BCI is based on Hebbian principles of associativity that...

In 2013–2014 we have advanced our MRCP-based BCI by demonstrating: (1) the ability to detect movement intent during dynamic tasks; (2) better detection accuracy than conventional approaches by implementing the locality preserving projection (LPP) approach; (3) the ability to use a single channel for accurate detection; and (4) enhanced neuroplastic...

Brain-computer interfaces can be used for motor substitution and recovery; therefore, detection and classification of movement intention is crucial for optimal control. In this study, palmar, lateral and pinch grasps were differentiated from the idle state and classified from single-trial EEG using only information prior the movement onset. Fourtee...

Closed-loop BCIs have recently been proposed for neurorehabilitation. This concept can be extended to complex motor tasks by decoding the type of the attempted movement in a multi-class BCI. Therefore, the objective of this study was to detect movements from real-time EEG and classify two movement types associated with the movement kinetics. EEG tr...

Objective. To detect movement intention from executed and imaginary palmar grasps in healthy subjects and attempted executions in stroke patients using one EEG channel. Moreover, movement force and speed were also decoded. Approach. Fifteen healthy subjects performed motor execution and imagination of four types of palmar grasps. In addition, five...

Objective. The possibility of detecting movement-related cortical potentials (MRCPs) at the single trial level has been explored for closing the motor control loop with brain–computer interfaces (BCIs) for neurorehabilitation. A distinct feature of MRCPs is that the movement kinetic information is encoded in the brain potential prior to the onset o...

Farina & Natalie Mrachacz-Kersting (2015): Detection of movement intention from single-trial movement-related cortical potentials using random and non-random paradigms, Brain-Computer Interfaces,

Alterations in attention are known to modify excitability of underlying cortical structures and thus the activity recorded during non-invasive electroencephalography (EEG). Brain-Computer-Interface systems for neuromodulation are based on reliable detection of intended movements from continuous EEG signals, thereby generating real time feedback to...

In recent years, movement related cortical potentials (MRCP), a type of slow cortical potentials, have been used for motor intention detection for triggering external devices in close loop rehabilitation paradigm. One of the main issues with these slow frequency MRCP signals is to separate them from the background brain activity or their poor signa...

In recent years, movement related cortical potentials (MRCP), a type of slow cortical potentials, have been used for motor intention detection for triggering external devices in close loop rehabilitation paradigm. One of the main issues with these slow frequency MRCP signals is to separate them from the background brain activity or their poor signa...

Detection of movement intention from the movement-related cortical potential (MRCP) derived from the electroencephalogram (EEG) signals has shown to be important in combination with assistive devices for effective neurofeedback in rehabilitation. In this study, we compare time and frequency domain features, extraction and classification, to detect...

This paper presents a novel method for feature extraction from electroencephalogram (EEG) signals using empirical mode decomposition (EMD). Its use is motivated by the fact that the EMD gives an effective time-frequency analysis of non-stationary signals. The intrinsic mode functions (IMF) obtained as a result of EMD give the decomposition of a sig...

Brain-Computer Interfacing is a promising approach to aid the rehabilitation process of patients suffering the consequences of neurological injuries. It has been shown in recent literature that a closed-loop setup utilizing the detection of movement-related cortical potentials (MRCP) to generate afferent feedback can efficiently help the stroke pat...

For the past decade, our group worked towards the development of a non-invasive BCI system for neuromodulation. Until recently, BCIs have been used mainly for communication and replacement or restoration of lost functions for severely disabled people. Using a BCI for neuromodulation requires that the protocol closely matches the steps involved in t...

We present a feasibility study of inducing plasticity of the corticospinal tract to the flexor carpi radialis muscle by using EEG based Brain-Computer Interface (BCI) technology. The mental task that drives the proposed asynchronous BCI is hand motor imagery, which when detected activates a functional electrical stimulation induced matching movemen...

Accurate detection and classification of force and speed intention in Movement Related Cortical Potentials (MRCPs) over a single trial offer a great potential for brain computer interface (BCI) based rehabilitation protocols. The MRCP is a non-stationary and dynamic signal comprising a mixture of frequencies with high noise susceptibility. The aim...

Non-invasive EEG-based Brain-Computer Interfaces (BCI) can be promising for the motor neuro-rehabilitation of paraplegic patients. However, this shall require detailed knowledge of the abnormalities in the EEG signatures of paraplegic patients. The association of abnormalities in different subgroups of patients and their relation to the sensorimoto...

he extraction of intended kinetic information from an EEG signal can have several applications related to the rehabilitation for subjects with various neurological disorders. However, the task is mainly constrained by the low signal-to-noise ratio for the EEG signals. It is well known that the cortical activity takes place at a very low frequency s...

Objective Applications of brain computer interfacing (BCI) in neurorehabilitation have received increasing attention. The intention to perform a motor task can be detected from scalp EEG and used to control rehabilitation devices, resulting in a patient-driven rehabilitation paradigm. In this study, we present and validate a BCI system for detectio...

Detecting movement intentions from Electroencephalography (EEG) signals and extracting intended kinetic information such as force and speed may have implications for rehabilitation with assistive technologies by casually linking afferent feedback from the assistive device with the cortical generated movement potentials. However, extraction and clas...

In this study we have explored the two EEG phenomena that accompany movement preparation and execution: movement related cortical potentials (MRCP) and event-related desynchronization/synchronization (ERD/ERS). The experiments comprised the two conditions for motor task initiation, self paced and cued. The aim of the study was to explore how the in...

In this study, we compared the effects of two imagery paradigms typically used within the field of brain computer interfaces on the detection of movement intention from scalp electroencephalography (EEG). This issue is important in the rehabilitation area because of its direct relation with appropriately timed neurofeedback. Subjects were asked to...

Utilizing transcranial magnetic stimulation (TMS) protocols prior to and after spinal manipulation, alterations in the activity within specific intracortical facilitatory and intracortical inhibitory pathways have been observed to an upper limb muscle (abductor pollicis brevis; APB). This study sought to investigate whether the previously shown mot...

In this study, the aim was to estimate the performance of a brain-computer interface (BCI) system by detecting movement intentions using only a single monopolar channel of electroencephalography (EEG). Seven healthy subjects performed four types of cued palmar grasps with two levels of force and speed. The movement intentions were detected using a...

This paper summarizes the research at Aalborg University within brain computer interfaces (BCI) used for rehabilitation done within the period 2006-2013. The work is based on movement related cortical potentials (MRCPs). MRCP's characterization for different task types was conducted in Nascimento et al [9] and showed that they potentially can be us...

We present a novel brain-computer interface for neuromodulation that leads to long lasting cortical plasticity. The system entails in recording the movement-related cortical potential (MRCP) as a subject imagines a dorsiflexion task and triggering an electrical stimulator to generate a single stimulus to the target nerve. This system has been teste...

Objective. In this study, the objective was to detect movement intentions and extract different levels of force and speed of the intended movement from scalp electroencephalography (EEG). We then estimated the performance of the closed loop system. Approach. Cued movements were detected from continuous EEG recordings using a template of the initial...

Brain—computer interface (BCI) systems aim at providing a nonmuscular communication and control channel to patients with severe disabilities or at promoting neuroplasticity. Motor imagery is the most common approach to producing electroencephalogram (EEG) changes in EEG-based BCI research. This chapter discusses an alternative approach for distingu...

In this work, we classified movement-related cortical potentials (MRCPs) associated with two levels of task force and speed with a linear and an optimized support vector machine (SVM). Features were extracted using Approximate Entropy (ApEn), Sample Entropy (SaEn) and Permutation Entropy (PeEn) calculated from the initial negative phase of the MRCP...

Objective: To understand the brain motor functions and neurophysiological changes due to motor disorder by comparing electroencephalographic data between healthy people and amyotrophic lateral sclerosis (ALS) patients. Methods: The movement related cortical potential (MRCP) was recorded from seven healthy subjects and four ALS patients. They were a...

To allow a routinely use of brain-computer interfaces (BCI), there is a need to reduce or completely eliminate the time-consuming part of the individualized training of the user. In this study, we investigate the possibility of avoiding the individual training phase in the detection of movement intention in asynchronous BCIs based on movement-relat...

This paper presents the design and implementation of a control strategy for an autonomous wheelchair to assist individuals suffering from severe motor disabilities. The user is presented with a pre-generated map of a known area (e.g. home, office) displayed on a computer screen, on which the location of the wheelchair is shown. Using a specially de...

We present a novel rehabilitation strategy based on LTP-like plasticity applied to 13 chronic stroke patients. Patients attended 3 sessions where they were asked to attempt a simple dorsiflexion task 50 times while the generated movement related cortical potentials (MRCP) were recorded using EEG. A single peripheral nerve stimulus was applied to th...

To allow a routinely use of brain–computer interfaces (BCI), there is a need to reduce or completely eliminate the time-consuming part of the individualized training of the user. In this study, we investigate the possibility of avoiding the individual training phase in the detection of movement intention in asynchronous BCIs based on movement-relat...

We present a novel rehabilitation strategy based on LTP-like plasticity applied to 13 chronic stroke patients. Patients attended 3 sessions where they were asked to attempt a simple dorsiflexion task 50 times while the generated movement potentials (MRCP) were recorded using scalp electrodes. A single peripheral nerve stimulus was applied to the co...

This paper proposes the development and experimental tests of a self-paced asynchronous brain-computer interfacing (BCI) system that detects movement related cortical potentials (MRCPs) produced during motor imagination of ankle dorsiflexion and triggers peripheral electrical stimulations timed with the occurrence of MRCPs to induce corticospinal p...

Detection of movement intention from neural signals combined with assistive technologies may be used for effective neurofeedback in rehabilitation. In order to promote plasticity, a causal relation between intended actions (detected for example from the EEG) and the corresponding feedback should be established. This requires reliable detection of m...

The study compared the movement related cortical potential (MRCP) of healthy subjects with that of amyotrophic lateral sclerosis (ALS) patients. We applied the same experimental and analytical methods to 7 healthy subjects and 4 ALS patients. They were asked to imagine right wrist extension at two speeds (fast and slow). The peak negativity and reb...

Brain-Computer Interface (BCI) provides new means of communication for people with motor disabilities by utilizing electroencephalographic activity. Selection of features from Electroencephalogram (EEG) signals for classification plays a key part in the development of BCI systems. In this paper, we present a feature selection strategy consisting of...

The aim of this study was to compare methods for feature extraction and classification of EEG signals for a brain-computer interface (BCI) driven by auditory and spatial navigation imagery. Features were extracted using autoregressive modeling and optimized discrete wavelet transform. The features were selected with exhaustive search, from the comb...

Objectives: It has been proposed that the alpha sleep anomaly plays a pathogenic role in fibromyalgia [F]. We have reexamined this question using digitized sleep recording to quantify the alpha EEG-band in the various stages of sleep. Methods: Sleep recordings from 20 women with F and 10 age matched normals [N] were used. All epochs without arousal...

Objectives: Based on visual ratings of the electroencephalography [EEG], the alpha EEG sleep anomaly has been described in subjects suffering from the fibromyalgia syndrome [FMS]. The aim of this study was to quantify the alpha EEG in patients with FMS compared to normal subjects using ambulatory recordings. Methods: Twelve women with FMS and 14 ag...

Objectives: Various sleep and related daytime complaints are frequent in patients with rheumatoid arthritis [RA]- and fibromyalgia syndrome [FMS], but also in the general population. The aim of this study was to compare the prevalence of these symptoms in the two patient groups to a control population. Methods: Forty-four women with RA, 43 with FMS...

The study investigated the possibility of identifying the speed of an imagined movement from EEG recordings in amyotrophic lateral sclerosis (ALS) patients. EEG signals were acquired from four ALS patients during imagination of wrist extensions at two speeds (fast and slow), each repeated up to 100 times in random order. The movement-related cortic...

The study explored the possibility of identifying movement type and speed from EEG recordings. EEG signals were acquired from 9 healthy volunteers during imagination of four tasks of the right wrist that involved two speeds (fast and slow) and two types of movement (wrist extension and rotation), each repeated 60 times in random order. Average move...

In this paper we introduce Smario, a MATLAB open source toolbox for the analysis of BCI signals and implementation of translation algorithms for BCI systems. The Smario functions have been created based on the design of EEGLAB, they are accessible through the graphic user interface but they can also be run and edited using MATLAB syntax. Smario rea...

Brain-Computer Interface (BCI) technology aims at providing communication and control facilities to severely paralyzed people. These patients are not able to manipulate objects or communicate their needs, even though their mental capabilities are intact. Electroencephalographic (EEG) signals recorded from the scalp can be used to decode wishes and...

Features extracted with optimized wavelets were compared with standard methods for a Brain-Computer Interface driven by non-motor imagery tasks. Two non-motor imagery tasks were used, Auditory Imagery of a familiar tune and Spatial Navigation Imagery through a familiar environment. The aims of this study were to evaluate which method extracts featu...

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A full M.Sc- curriculum in Biomedical Engineering and Informatics was established at Aalborg University in 2000. The curriculum reflects the multidisciplinary composition of Biomedical Engineering and it consists of elements from engineering, informatics, medical and natural sciences as well as of elements from the social sciences. The education fo...

This paper summarizes the brain-computer interface (BCI)-related research being conducted at Aalborg University. Namely, an online synchronized BCI system using steady-state visual evoked potentials, and investigations on cortical modulation of movement-related parameters are presented.

Topographical organization in the neocortex shows experience-dependent plasticity. We hypothesized that experimental sensitization of the esophagus results in changes of the topographical distribution of the evoked potentials and the corresponding dipole source activities to painful stimulation. An endoscopic method was used to deliver 35 electrica...

A multitude of studies have demonstrated a clear activation of the motor cortex during imagination of various motor tasks; however, it is still unclear if movement-related parameters (movement direction, range of motion, speed, force level and rate of force development) specifically modulate cortical activation as they do during the execution of ac...

Independent component analysis (ICA) of the electroencephalogram (EEG) overcomes many of the classical problems in EEG analysis. We used ICA to determine the brain responses to painful stimulation of the oesophagus. Twelve subjects with a median age of 41 years were included. With a nasal endoscope, two series of 35 electrical stimuli at the pain t...