Ramaswamy Palaniappan

• PhD
• Professor (Associate) at University of Kent

Previous research indicates that human salivary responses habituate to repeated exposure to visual, olfactory, or gustatory food cues in adults and children. This study aims to delve into the neurophysiological dynamics of within-session attentional habituation and its correlation with Body Mass Index (BMI) concerning the repetition of high and low...

Transcutaneous auricular vagus nerve stimulation (taVNS), a non-invasive form of electrical brain stimulation, has shown potent therapeutic potential for a wide spectrum of conditions. How taVNS influences the characterization of motion sickness – a long mysterious syndrome with a polysymptomatic onset – remains unclear. Here, to examine taVNS-indu...

Users with Autism Spectrum Disorder may find human-computer interaction (HCI) challenging due to a number of symptoms experienced, one of which is difficulty coping with change. Through capturing, comparing and statistically analysing the reactions of autistic and neurotypical users to seven individual design changes, it has been possible to identi...

Transcutaneous auricular vagus nerve stimulation (taVNS), a non-invasive form of electrical brain stimulation, has shown potent therapeutic potential for a wide spectrum of conditions. How taVNS influences the characterization of motion sickness - a long mysterious syndrome with a polysymptomatic onset - remains unclear. Here, to examine taVNS-indu...

Perturbations in the autonomic nervous system occur in individuals experiencing increasing levels of motion sickness. Here, we investigated the effects of transauricular electrical stimulation (tES) on autonomic function during visually induced motion sickness, through the analysis of spectral and time-frequency heart rate variability. To determine...

Hand grasp recognition with surface electromyography (sEMG) has been used as a possible natural strategy to control hand prosthetics. However, effectively performing activities of daily living for users relies significantly on the long-term robustness of such recognition, which is still a challenging task due to confused classes and several other v...

Transcutaneous auricular vagus nerve stimulation (taVNS) is a potent therapeutic tool for a broad spectrum of diseases and disorders. Here, we investigated the effects of taVNS on autonomic function during visually induced motion sickness, through the analysis of spectral and time-frequency heart rate variability. To determine the efficacy of taVNS...

p>Hand grasp recognition with surface electromyography (sEMG) has been used as a possible natural strategy to control hand prosthetics. However, effectively performing activities of daily living for users relies significantly on the long-term robustness of such recognition, which is still a challenging task due to confused classes and several othe...

p>Hand grasp recognition with surface electromyography (sEMG) has been used as a possible natural strategy to control hand prosthetics. However, effectively performing activities of daily living for users relies significantly on the long-term robustness of such recognition, which is still a challenging task due to confused classes and several othe...

This study examines the neural activities of participants undergoing vibro-motor reprocessing therapy (VRT) while experiencing motion sickness. We evaluated the efficacy of vibro-motor reprocessing therapy, a novel therapeutic technique based on eye movement desensitization and reprocessing (EMDR), in reducing motion sickness. Based on visually ind...

Hand gesture recognition with surface electromyography (sEMG) is indispensable for Muscle-Gesture-Computer Interface. The usual focus of it is upon performance evaluation involving the accuracy and robustness of hand gesture recognition. However, addressing the reliability of such classifiers has been absent, to our best knowledge. This may be due...

Food variety influences appetitive behaviour, motivation to eat and energy intake. Research found that repeated exposure to varied food images increases the motivation towards food in adults and children. This study investigates the effects of repetition on the modulation of early and late components of event-related potentials (ERPs) when particip...

Passive detection of footsteps in domestic settings can allow the development of assistive technologies that can monitor mobility patterns of older adults in their home environment. Acoustic footstep detection is a promising approach for non-intrusive detection of footsteps. So far there has been limited work in developing robust acoustic footstep...

Half of all road accidents result from either lack of driver attention or from maintaining insufficient separation between vehicles. Collision from the rear, in particular, has been identified as the most common class of accident in the UK, and its influencing factors have been widely studied for many years. Rear-mounted stop lamps, illuminated whe...

This study explores auricular vagus nerve stimulation (aVNS) within the context of stress. Five healthy subjects underwent a pulsed mode aVNS (with a frequency of 25 Hz and pulse width of 200 μs ) using a custom made current stimulation device. The device triggered the auricular vagus nerve branch through the tragus for 15 minutes, with prefrontal...

This paper presents and explores a robust deep learning framework for auscultation analysis. This aims to classify anomalies in respiratory cycles and detect diseases, from respiratory sound recordings. The framework begins with front-end feature extraction that transforms input sound into a spectrogram representation. Then, a back-end deep learnin...

This study explores auricular vagus nerve stimulation (aVNS) within the context of stress. Five healthy subjects underwent a pulsed mode aVNS (with a frequency of 25 Hz and pulse width of 200 μs ) using a custom made current stimulation device. The device triggered the auricular vagus nerve branch through the tragus for 15 minutes, with prefrontal...

Rear-end collision accounts for around 8% of all vehicle crashes in the UK, with the failure to notice or react to a brake light signal being a major contributory cause. Meanwhile traditional incandescent brake light bulbs on vehicles are increasingly being replaced by a profusion of designs featuring LEDs. In this paper, we investigate the efficac...

This article proposes an encoder-decoder network model for Acoustic Scene Classification (ASC), the task of identifying the scene of an audio recording from its acoustic signature. We make use of multiple low-level spectrogram features at the front-end, transformed into higher level features through a well-trained CNN-DNN front-end encoder. The hig...

Steady State Visual Evoked Potential (SSVEP) methods for brain–computer interfaces (BCI) are popular due to higher information transfer rate and easier setup with minimal training, compared to alternative methods. With precisely generated visual stimulus frequency, it is possible to translate brain signals into external actions or signals. Traditio...

Rear-end collision accounts for around 8% of all vehicle crashes in the UK, with the failure to notice or react to a brake light signal being a major contributory cause. Meanwhile traditional incandescent brake light bulbs on vehicles are increasingly being replaced by a profusion of designs featuring LEDs. In this paper, we investigate the efficac...

This work demonstrates the effectiveness of Convolutional Neural Networks in the task of pose estimation from Electromyographical (EMG) data. The Ninapro DB5 dataset was used to train the model to predict the hand pose from EMG data. The models predict the hand pose with an error rate of 4.6% for the EMG model, and 3.6% when accelerometry data is i...

Recently, the subject-specific surface electromyography (sEMG)-based gesture classification with deep learning algorithms has been widely researched. However, it is not practical to obtain the training data by requiring a user to perform hand gestures many times in real life. This problem can be alleviated to a certain extent if sEMG from many othe...

This paper presents a robust deep learning framework developed to detect respiratory diseases from recordings of respiratory sounds. The complete detection process firstly involves front end feature extraction where recordings are transformed into spectrograms that convey both spectral and temporal information. Then a back-end deep learning model c...

A fully customisable chip-on board (COB) LED design to evoke two brain responses simultaneously (steady state visual evoked potential (SSVEP) and transient evoked potential, P300) is discussed in this paper. Considering different possible modalities in brain-computer interfacing (BCI), SSVEP is widely accepted as it requires a lesser number of elec...

This paper explores the use of three different two-dimensional time–frequency features for audio event classification with deep neural network back-end classifiers. The evaluations use spectrogram, cochleogram and constant-Q transform-based images for classification of 50 classes of audio events in varying levels of acoustic background noise, revea...

In this work, we propose an approach that features deep feature embedding learning and hierarchical classification with triplet loss function for Acoustic Scene Classification (ASC). In the one hand, a deep convolutional neural network is firstly trained to learn a feature embedding from scene audio signals. Via the trained convolutional neural net...

This article proposes an encoder-decoder network model for Acoustic Scene Classification (ASC), the task of identifying the scene of an audio recording from its acoustic signature. We make use of multiple low-level spectrogram features at the front-end, transformed into higher level features through a well-trained CNN-DNN front-end encoder. The hig...

This paper presents a robust deep learning framework developed to detect respiratory diseases from recordings of respiratory sounds. The complete detection process firstly involves front end feature extraction where recordings are transformed into spectrograms that convey both spectral and temporal information. Then a back-end deep learning model c...

Healthcare field is highly benefited by incorporating BCI for detection and diagnosis of some health related detriment as well as rehabilitation and restoration of certain disabilities. An EEG dataset acquired from 15 high-functioning ASD patients, while they were undergoing a P300 experiment in a virtual reality platform, was analysed in this pape...

The students’ progression and attainment gap are considered as key performance indicators of many universities worldwide. Therefore, universities invest significantly in resources to reduce the attainment gap between good and poor performing students. In this regard, various mathematical models have been utilised to predict students’ performances i...

Understanding how difficult a learning task is for a person allows teaching material to be appropriately designed to suit the person, especially for programming material. A first step for this would be to predict on the task difficulty level. While this is possible through subjective questionnaire, it could lead to misleading outcome and it would b...

Currently, carbon dioxide (CO2) waveforms measured by capnography are used to estimate respiratory rate and end-tidal CO2 (EtCO2) in the clinic. However, the shape of the CO2 signal carries significant diagnostic information about the asthmatic condition. Previous studies have shown a strong correlation between various features that quantitatively...

s disease (PD) is a neurodegenerative disorder which is mainly caused by the loss of neurotransmitters in basal ganglia and substantia nigra in brain. PD adversely aects the quality of life of nearly six million people all over the world. e primary symptoms of PD are tremor, muscular rigidity, bradykinesia (i.e., slowness of movement), and postural...

Hybrid BCI Utilising SSVEP and P300 Event Markers for Reliable and Improved Classification Using LED Stimuli

The objective of this research was to investigate the relationship between emotion recognition and lateralization of motor onset in Parkinson?s disease (PD) patients using electroencephalogram (EEG) signals. The subject pool consisted of twenty PD patients [ten with predominantly left-sided (LPD) and ten with predominantly right-sided (RPD) motor s...

Existing research on task difficulty and program comprehension mainly concentrate on brain areas related to attention and meditation. In this research, an in-depth analysis of Task Difficulty Level (TDL) for program comprehension is proposed with features extracted from different areas of the brain. Two levels of task difficulty were analysed: easy...

Steady state visual evoked response (SSVEP) is widely used in visual based diagnosis and applications such as brain-computer interfacing due to its high information transfer rate and the capability to activate commands through simple gaze control. However, one major impediment in using flashing visual stimulus to obtain SSVEP is eye fatigue that pr...

Electrical activities from brain (electroencephalogram, EEG) and heart (electrocardiogram, ECG) have been proposed as biometric modalities but the combined use of these signals appear not to have been studied thoroughly. Also, the feature stability of these signals has been a limiting factor for biometric usage. This paper presents results from a p...

This study investigates the influence of LED visual stimulus brightness in evoking steady state visual evoked potential (SSVEP) responses in brain which can be utilised for vision research, medical diagnostics or for developing brain-computer interfaces (BCI). LED visual stimulus was based on a radial 130 mm chip on board (COB) LEDs emitting green...

This study investigates the influence of LED visual stimulus brightness in evoking steady state visual evoked potential (SSVEP) responses in brain which can be utilised for vision research, medical diagnostics or for developing brain-computer interfaces (BCI). LED visual stimulus was based on a radial 130 mm chip on board (COB) LEDs emitting green...

This study proposes the use of radial visual stimuli design to obtain increased steady state visual evoked potential (SSVEP) responses that can be utilised in brain-computer interfaces (BCI). Visual stimuli designs based on chip on board (COB) LEDs were used in this study to compare the influences of the radial with horizontal and concentric patter...

In this paper, we explore the visual effects of animated 2D line strokes and 3D cubes. A given 2D image is segmented into either 2D line strokes or 3D cubes. Each segmented object (i.e., line stroke or each cube) is initialised with the position and the colour of the corresponding pixel in the image. The program animates these objects using the boi...

Electrical activities from brain (electroencephalogram, EEG) and heart (electrocardiogram, ECG) have been proposed as biometric modalities but the combined use of these signals appear not to have been studied thoroughly. Also, the feature stability of these signals has been a limiting factor for biometric usage. This paper presents results from a p...

Steady state visual evoked potential (SSVEP) is extensively used in the research of brain-computer interface (BCI) and require a controllable and configurable light source. SSVEP requires appropriate control of visual stimulus parameters, such as icker frequency, light intensity, multi-frequency light source and multi-spectral compositions. Light...

Multiresolution analysis (MRA) over graph representation of EEG data has proved to be a promising method for offline brain-computer interfacing (BCI) data analysis. For the first time we aim to prove the feasibility of the graph lifting transform in an online BCI system. Instead of developing a pointer device or a wheel-chair controller as test bed...

This article focuses on different orientations of LED visual stimulus configurations which could be used to elicit Steady State Visual Evoked Potentials (SSVEP). SSVEP is extensively used in the research for various biomedical applications and require a configurable light source flickering at a constant frequency that would induce responses in corr...

Products based on binaural beat technology are popular as these claim to relax the user by altering (i.e. entraining) the brain’s neuronal rhythm and a Google search will result in more than a million hits [1]. Whilst this is possible in certain approaches, for example in photic frequency following effects such as in brain-computer interfaces [2];...

In this paper, it is shown that classification of features from heart (electrocardiogram, ECG) signals for biometric purposes (i.e. for individual identification) degrades over a period of time and a method based on binaural brain entrainment is proposed to minimise the variations in the heart signals over time to improve the classification perform...

In addition to classic motor signs and symptoms, individuals with Parkinson's disease (PD) are characterized by emotional deficits. Ongoing brain activity can be recorded by electroencephalograph (EEG) to discover the links between emotional states and brain activity. This study utilized machine-learning algorithms to categorize emotional states in...

Electroencephalogram (EEG) signals are useful for diagnosing various mental conditions such as epilepsy, memory impairments and sleep disorders. Brain–computer interface (BCI) is a revolutionary new area using EEG that is most useful for the severely disabled individuals for hands-off device control and communication as they create a direct interfa...

Steady state visually evoked potentials (SSVEP) are extensively used in the research of brain-computer interface (BCI) and require a configurable light source flashing at different frequencies. Precise control of simultaneous multiple frequencies are essential for SSVEP studies and also for reducing the visual fatigue. Instead of LCD based stimulus...

Parkinson’s disease (PD) is not only characterized by its prominent motor symptoms but also associated with disturbances in cognitive and emotional functioning. The objective of the present study was to investigate the influence of emotion processing on inter-hemispheric electroencephalography (EEG) coherence in PD. Multimodal emotional stimuli (ha...

While Parkinson's disease (PD) has traditionally been described as a movement disorder, there is growing evidence of disruption in emotion information processing associated with the disease. The aim of this study was to investigate whether there are specific electroencephalographic (EEG) characteristics that discriminate PD patients and normal cont...

Brain-computer interfaces (BCI) are useful devices that allow direct control of external devices using thoughts, i.e. brain's electrical activity. There are several BCI paradigms, of which steady state visual evoked potential (SSVEP) is the most commonly used due to its quick response and accuracy. SSVEP stimuli are typically generated by varying t...

Deficits in the ability to process emotions characterize several neuropsychiatric disorders and are traits of Parkinson's disease (PD), and there is need for a method of quantifying emotion, which is currently performed by clinical diagnosis. Electroencephalogram (EEG) signals, being an activity of central nervous system (CNS), can reflect the unde...

The imagination of limb movements offers an intuitive paradigm for the control of electronic devices via brain computer interfacing (BCI). The analysis of electroencephalographic (EEG) data related to motor imagery potentials has proved to be a difficult task. EEG readings are noisy, and the elicited patterns occur in different parts of the scalp,...

In a previous study, it has been shown that brain activity, i.e., electroencephalogram (EEG) signals, can be used to generate personal identification number (PIN). The method was based on brain-computer interface (BCI) technology using a P300-based BCI approach and showed that a single-channel EEG was sufficient to generate PIN without any error fo...

Brain computer interfaces are control systems that allow the interaction with electronic devices by analysing the user's brain activity. The analysis of brain signals, more concretely, electroencephalographic data, represents a big challenge due to its noisy and low amplitude nature. Many researchers in the field have applied wavelet transform in o...

Assessment of awareness for those with disorders of consciousness is a challenging undertaking, due to the complex presentation of the population. Debate surrounds whether behavioral assessments provide greatest accuracy in diagnosis compared to neuro-imaging methods, and despite developments in both, misdiagnosis rates remain high. Music therapy m...

Brain computer interfaces (BCI) provide a new approach to human computer communication, where the control is realised via performing mental tasks such as motor imagery (MI). In this study, we investigate a novel method to automatically segment electroencephalographic (EEG) data within a trial and extract features accordingly in order to improve the...

Among the many paradigms used in brain-computer interface (BCI), steady state visual evoked potential (SSVEP) offers the quickest response; however it is disadvantageous from the point of view of visual fatigue, which prevents subjects from prolonged usage of visual stimuli especially when LEDs are used. In this paper, we propose a visual stimulato...

Objective. Multiresolution analysis (MRA) offers a useful framework for signal analysis in the temporal and spectral domains, although commonly employed MRA methods may not be the best approach for brain computer interface (BCI) applications. This study aims to develop a new MRA system for extracting tempo-spatial-spectral features for BCI applicat...

This study addresses two important problem statements namely, selection of training datasets for online Brain-computer Interface (BCI) classifier training and determination of participant concentration levels during an experiment. The work discusses a novel integration of Electroencephalogram (EEG) and Near Infra Red Spectroscopy (NIRS) for possibl...

This study addresses two important problem statements namely, selection of training datasets for online Brain-computer Interface (BCI) classifier training and determination of participant concentration levels during an experiment. The work also attempted a pilot study to integrate Electroencephalogram (EEG) and Near Infra Red Spectroscopy (NIRS) fo...

This study investigates the indications of nonlinear dynamic structures in electroencephalogram signals. The iterative amplitude adjusted surrogate data method along with seven nonlinear test statistics namely the third order autocorrelation, asymmetry due to time reversal, delay vector variance method, correlation dimension, largest Lyapunov expon...

Genetic Algorithms (GAs) were used in a previous study to automate parameter selection for an EEG-based P300-driven Brain-Computer Interface (BCI). The GA approach showed marked improvement over data-insensitive parameter selection; however, it required lengthy execution times thereby rendering it infeasible for online implementation. Automated par...

Electrooculogram (EOG) signals have been used in designing Human-Computer Interfaces, though not as popularly as electroencephalogram (EEG) or electromyogram (EMG) signals. This paper explores several strategies for improving the analysis of EOG signals. This article explores its utilization for the extraction of features from EOG signals compared...

Brain computer interfacing (BCI) offers the possibility to interact with machines uniquely relying on the user's thoughts. Although wavelet analysis has been used in the BCI field there is evidence that standard wavelet families, such as Daubechies, may not be the optimal approach. In this study, we developed a novel wavelet lifting scheme, specifi...

Automatic identification of a person's individuality is an important issue today. Brain Computer Interfaces (BCI) which uses EEG as a modality is a promising area for cognitive biometrics. A BCI system could be used to recognise a sequence (say letters, colours or images) by the user. This sequence could form a 'BrainWord', which could be used for...

The classification stage of a Brain-Computer Interface (BCI), in general, is the only avenue through which a BCI are tuned to the user's unique brain activity. Subject-specific BCI tuning is necessary in order to cater to inherent inter-subject physiological differences that manifest itself in the EEG. However, the pre-processing and feature extrac...

This paper investigates a method to generate personal identification number (PIN) using brain activity recorded from a single active electroencephalogram (EEG) channel. EEG based biometric to generate PIN is less prone to fraud and the method is based on the recent developments in brain-computer interface (BCI) technology, specifically P300 based B...

Electrooculogram (EOG) signals have been used in designing Human-Computer Interfaces, though not as popularly as electroencephalogram (EEG) or electromyogram (EMG) signals. This paper explores several strategies for improving the analysis of EOG signals. This article explores its utilization for the extraction of features from EOG signals compared...

This paper reports on the development of a proof-of-concept brain-computer music interfacing system (BCMI), which we built to be tested with a patient with Locked-in Syndrome at the Royal Hospital for Neuro-disability, in London. The system uses the Steady State Visual Evoked Potential (SSVEP) method, whereby targets are presented to a user on a co...

The steady state visual evoked protocol has recently become a popular paradigm in brain-computer interface (BCI) applications. Typically (regardless of function) these applications offer the user a binary selection of targets that perform correspondingly discrete actions. Such discrete control systems are appropriate for applications that are inher...