GKP Signal Processing Using Deep CNN and SVM for Tongue-Machine Interface

The tongue is one of the few organs with high mobility in the case of severe spinal cord injuries. However, most tongue-machine interfaces (TMIs) require the patient to wear obtrusive and unhygienic devices in and around the mouth. This paper aims to develop a TMI based on the glossokinetic potentials (GKPs), i.e. the electrical signals generated by the tongue when it touches the buccal walls. Ten patients were recruited for this research. The GKP patterns were classified by convolutional neural network (CNN) and support vector machine (SVM). It was observed that the CNN outperformed the SVM in individual and average scores for both raw and preprocessed datasets, reaching an accuracy of 97 similar to 99%. The CNN-based GKP processing method makes it easy to build a natural, appealing and robust TMI for the paralyzed. Being the first attempt to process GKPs with the CNN, our research offers an alternative to the traditional brain-computer interfaces (BCIs), which suffers from the instability and low signal-to-noise ratio (SNR) of electroencephalography (EEG)

Evoked potentials to syllable perception and production

The purpose of this study was to devise and test a methodology to investigate cortical activity during speech perception and production that eliminates some of the confounding variables that have existed in previous research, such as the variability of stimuli employed across conditions and the role of muscle artifacts; and to clarify the role of auditory feedback in speech. Evoked potentials to the speech stimulus /ba/ were obtained while the subject was hearing and speaking /ba/ with and without immediate air conducted feedback, as well as while hearing /ba/ 0.6 sec. after each of these three conditions. Twelve adults were determined to have dominant left hemispheres through a series of seven hemispheric dominance tests. None had a history of a hearing deficiency or indicated a hearing loss during the practice session. Monopolar recordings were made from the left and right frontal areas corresponding to Broca's area on the left and the left and right temporoparietal areas posterior to the termination of the Sylvian fissure, with a linked earlobe reference

Brain computer interface based neurorehabilitation technique using a commercially available EEG headset

Neurorehabilitation has recently been augmented with the use of virtual reality and rehabilitation robotics. In many systems, some known volitional control must exist in order to synchronize the user intended movement with the therapeutic virtual or robotic movement. Brain Computer Interface (BCI) aims to open up a new rehabilitation option for clinical population having no residual movement due to disease or injury to the central or peripheral nervous system. Brain activity contains a wide variety of electrical signals which can be acquired using many invasive and non-invasive acquisition techniques and holds the potential to be used as an input to BCI. Electroencephalogram (EEG) is a non-invasive method of acquiring brain activity which then, with further processing and classification, can be used to predict various brain states such as an intended motor movement. EEG provides the temporal resolution required to obtain significant result which may not be provided by many other non-invasive techniques. Here, EEG is recorded using a commercially available EEG headset provided by Emotiv Inc. Data is collected and processed using BCI2000 software, and the difference in the Mu-rhythm due to Event Related Synchronization (ERS) and Desynchronization (ERD) is used to distinguish an intended motor movement and resting brain state, without the need for physical movement. The idea is to combine this user intent/free will with an assistive robot to achieve the user initiated, repetitive motor movements required to bring therapeutic changes in the targeted subject group, as per Hebbian type learning

EEG Signal Processing and Analysis

Tato práce se zabývá oblastí elektroencefalografie, zpracováním EEG signálů a jejich analýzou. Jsou vysvětleny základní principy vzniku biologických signálů v mozku, charakteristické mozkové vlny a jejich klasifikace. Dále práce ilustruje základní metodologie měření a záznamu těchto signálů, chyby měření, vliv a zdroje signálových artefaktů. Následně je rozebrána problematika předzpracování signálu, nejrozšířenější metodologie, jejich primární určení a teoretické podklady. Zároveň je obsažen i přehled metod pro analýzu EEG signálu v časové, frekvenční a časově-frekvenční oblasti. Jádrem práce jsou metody analýzy EEG signálu ve frekvenční oblasti, jsou uvedeny jejich teoretické podklady, omezení, odchylky a zaměření, jako i vhodné matematické aparáty pro kompenzaci uvedených nedostatků. Praktická část popisuje architekturu a implementaci aplikace Easy EEG Player, která vznikla jako součást téhle práce. Jsou popsány metody reprezentace, zpracováni a analýzy EEG dat za použití zvolených metodologií.This thesis covers topic of electroencephalography, EEG signal processing and analysis. It explains fundamental concepts of biological signal genesis in brain, characteristic brain waves and their classi cation. Then it illustrates basic methodologies of EEG signal recording, measurement errors, impact and sources of signal artifacts. Thesis provides overview of the most common methodologies for EEG preprocessing and analysis with special focus on methods for spectral analysis. Practical part of this thesis describes architecture and implementation of Easy EEG Player application created as a part of this thesis.

Event related (de-)synchronization patterns in actual and imagined hand movements

Projecte final de carrera realitzat en col.laboració amb Philips ResearchThis project presents different signal processing techniques, such as Principal Component Analysis (PCA) and Common Spatial Patterns (CSP), applied to characterize the reactivity of central rhythms in the alpha and beta bands during self paced voluntary and imaginary movement. The idea is to allow people to control devices, or interact with machines by simply thinking. To do so, we monitor the brain activity using electroencephalogram (EEG) measurements which record the signals from electrodes positioned on the scalp. The objective is to use motor imagery signals to build a brain computer interface, able to learn from data analyzed before, using the properties of neural networks. The possibility of designing an intuitive communication system between a brain and a computer, available to be operated by everyone, even by people with severe motor impairments, is the main objective of this stud

Biomedical signal filtering for noisy environments

 Luke\u27s work addresses issue of robustly attenuating multi-source noise from surface EEG signals using a novel Adaptive-Multiple-Reference Least-Means-Squares filter (AMR-LMS). In practice, the filter successfully removes electrical interference and muscle noise generated during movement which contaminates EEG, allowing subjects to maintain maximum mobility throughout signal acquisition and during the use of a Brain Computer Interface

An electroencephalographic investigation of the impact of eye movements in a change detection task

openIn studies involving Event-Related Potentials (ERPs), ocular artifacts such as blinks and saccades can compromise the quality of the recorded neural signals. To address this issue, researchers often manually reject epochs (that is a specific time-window extracted from the continuous EEG signal) containing these artifacts. However, this procedure consistently reduces the number of epochs that can be used for extracting ERPs. An alternative solution is to use Independent Component Analysis (ICA), which can preserve more epochs for analysis by removing only the artifact from the EEG recording. However, the reliability of ICA in neurocognitive studies of lateralized ERP components, such as the Sustained Posterior Contralateral Negativity (SPCN) related to visual working memory load, remains unclear, particularly in contexts where subjects are more likely to make saccades during the task. Furthermore, by using ICA, we are assuming that ocular movements do not interact with the neural signal, which has yet to be confirmed. For this reason, in the present experiment, all the participants were asked to perform a change detection task under two conditions: a ‘free gaze/saccade’ condition, where they were allowed to move their eyes to look at the lateralized stimuli, and a ‘fixation’ condition, where they were required to maintain the gaze on the center of the monitor. The subjects were also split into two groups, each performing the same experiment but with different stimulus presentation times (100 ms and 500 ms) to investigate whether saccades could differently affect the ERP in these conditions. The SPCN components were then extracted using both the Independent Component Analysis (ICA) correction and epoch-rejection methods. The results revealed that ICA correction is a robust and reliable method for experimental paradigms with a short presentation time of the stimuli (100 ms). By removing only the saccades, the features of the SPCN are preserved, suggesting that with this method we can retain a higher number of epochs for the ERP extraction with the certainty that saccades do not alter the neural signal.In studies involving Event-Related Potentials (ERPs), ocular artifacts such as blinks and saccades can compromise the quality of the recorded neural signals. To address this issue, researchers often manually reject epochs (that is a specific time-window extracted from the continuous EEG signal) containing these artifacts. However, this procedure consistently reduces the number of epochs that can be used for extracting ERPs. An alternative solution is to use Independent Component Analysis (ICA), which can preserve more epochs for analysis by removing only the artifact from the EEG recording. However, the reliability of ICA in neurocognitive studies of lateralized ERP components, such as the Sustained Posterior Contralateral Negativity (SPCN) related to visual working memory load, remains unclear, particularly in contexts where subjects are more likely to make saccades during the task. Furthermore, by using ICA, we are assuming that ocular movements do not interact with the neural signal, which has yet to be confirmed. For this reason, in the present experiment, all the participants were asked to perform a change detection task under two conditions: a ‘free gaze/saccade’ condition, where they were allowed to move their eyes to look at the lateralized stimuli, and a ‘fixation’ condition, where they were required to maintain the gaze on the center of the monitor. The subjects were also split into two groups, each performing the same experiment but with different stimulus presentation times (100 ms and 500 ms) to investigate whether saccades could differently affect the ERP in these conditions. The SPCN components were then extracted using both the Independent Component Analysis (ICA) correction and epoch-rejection methods. The results revealed that ICA correction is a robust and reliable method for experimental paradigms with a short presentation time of the stimuli (100 ms). By removing only the saccades, the features of the SPCN are preserved, suggesting that with this method we can retain a higher number of epochs for the ERP extraction with the certainty that saccades do not alter the neural signal

Analysis for Automatic Detection of Epileptic Seizure from EEG signals

During longterm EEG monitoring of epileptic patients, automatic detection methods could be of great assistance because they save a lot of time. The work was develop in cooperation with Micromed Spa with the aim of evaluate and compare the performance of two seizure detection algorithm:one using wavelet based features and one based on AR parameters. The Artificial Neural Network were used as classification method. The results show a better reliability for the ANN having in input AR parameter

Does a cognitive load metric predict behavioural and physiological responses in a working memory task? -

Tutkimuksen tavoitteena oli toistaa TBRS-työmuistimallin (Time-Based Resource-Sharing) esittelevässä tutkimuksessa (Barrouillet et al. 2004) havaitut tulokset. Malli esittää työmuistisillan olevan suoraan verrannollinen muistisuoritusta häiritsevän tiedonkäsittelytehtävän vaatimaan muistihakuja / aika -suhteeseen. Tutkimuksessa yritettiin myös selvittää, voiko tämän suhteen määrittelemän kognitiivisen kuorman vaikutuksia mitata fysiologisin menetelmin tutkimalla tehtävän aikana syntyneitä herätevasteita, erityisesti P300-aaltoa ja myöhäisiä positiivisia komponentteja. Tutkimuksessa ei onnistuttu toistamaan alkuperäisen tutkimuksen tuloksia. Havainnot eivät tukeneet TBRS-mallin ennustamaa kognitiivisen kuorman lineaarista vaikutusta työmuistisillan pituuteen. Herätevasteista havaittiin selkeät vaikutukset sekä kognitiiviselle, että muistikuormalle. Nämä havainnot eivät kuitenkaan tue ajatusta ristivaikutuksesta tiedonkäsittelyn ja muistisuorituksen välillä. Tulokset herättävät useita mielenkiintoisia tutkimuskysymyksiä tiedonkäsittelystä ja muistisuoriutumisesta, puheen vaikutuksesta työmuistitehtävissä suoriutumiseen sekä kognitiivisen kuorman vaikutuksesta.The goal of this study was to attempt to replicate the results obtained by Barrouillet et al. (2004) in a study introducing the Time-Based Resource-Sharing (TBRS) model, which postulates that working memory spans are positively and linearly correlated to the retrievals / time -ratio, i.e. cognitive load, a given processing task involves. We also attempted to determine whether the effects of cognitive load are measurable with neurophysiology by examining the event-related potentials (ERPs), in particular the P300 and late positive components. We were unable to replicate the results of the original study to any extent, and did not find an effect for cognitive load on working memory spans similar to that predicted by the TBRS model. Clear effects for cognitive and memory load were found in the ERPs, but these results did not indicate a trade-off between processing and storage. Questions for future research are presented regarding memory and processing performance, the role of speech in working memory tasks, and the effect of cognitive load