An Adaptive Task-Related Component Analysis Method for SSVEP recognition

Steady-state visual evoked potential (SSVEP) recognition methods are equipped with learning from the subject's calibration data, and they can achieve extra high performance in the SSVEP-based brain-computer interfaces (BCIs), however their performance deteriorate drastically if the calibration trials are insufficient. This study develops a new method to learn from limited calibration data and it proposes and evaluates a novel adaptive data-driven spatial filtering approach for enhancing SSVEPs detection. The spatial filter learned from each stimulus utilizes temporal information from the corresponding EEG trials. To introduce the temporal information into the overall procedure, an multitask learning approach, based on the bayesian framework, is adopted. The performance of the proposed method was evaluated into two publicly available benchmark datasets, and the results demonstrated that our method outperform competing methods by a significant margin.Comment: 23 pages, 3 Figures, 6 Table

SSVEP-Based BCIs

This chapter describes the method of flickering targets, eliciting fundamental frequency changes in the EEG signal of the subject, used to drive machine commands after interpretation of user’s intentions. The steady-state response of the changes in the EEG caused by events such as visual stimulus applied to the subject via a computer screen is called steady-state visually evoked potential (SSVEP). This feature of the EEG signal can be used to form a basis of input to assistive devices for locked in patients to improve their quality of life, as well as for performance enhancing devices for healthy subjects. The contents of this chapter describe the SSVEP stimuli; feature extraction techniques, feature classification techniques and a few applications based on SSVEP based BCI

Machine Learning-driven EEG Analysis towards brain-controlled vehicle

Με τη ραγδαία ανάπτυξη της τεχνολογίας, ο ανθρώπινος εγκέφαλος και οι υπολογιστές μπορούν να συνεργαστούν με τη βοήθεια βιοηλεκτρονικών συσκευών που χρησιμοποιούν βιο-σήματα, τα οποία ανιχνεύονται από μια συγκεκριμένη κατηγορία αισθητήρων που ονομάζονται βιο-αισθητήρες. Ένας νέος τομέας έρευνας που σχετίζεται με τη μελέτη των βιο-σημάτων έχει επικεντρωθεί ιδιαίτερα στην τεχνολογία ελεγχόμενη από το μυαλό. Πιο συγκεκριμένα, ο άμεσος έλεγχος ενός οχήματος με χρήση εγκεφαλικών κυμάτων μπορεί να βοηθήσει τα άτομα με αναπηρίες να ανακτήσουν τις οδηγικές τους ικανότητες, καθώς και να προσφέρει μια νέα επιλογή για υγιή άτομα να χειριστούν ένα όχημα. Η παρούσα πτυχιακή εργασία περιγράφει ένα όχημα ελεγχόμενο με το μυαλό (BCV) που χρησιμοποιεί την τεχνολογία Brain Computer Interface (BCI) για να ερμηνεύσει δεδομένα Ηλεκτροεγκεφαλογραφίας (EEG), να χειριστεί μια συσκευή και να αξιολογήσει τα εγκεφαλικά κύματα, προκειμένου να παραμείνει όσο το δυνατόν πιο κοντά στην ανθρώπινη φύση. Το σύστημα, το οποίο βασίζεται σε τεχνικές Μηχανικής Μάθησης, περιλαμβάνει τα ακόλουθα χαρακτηριστικά: (α) Επεξεργασία δεδομένων EEG για την ανάπτυξη διαφόρων μεθόδων εξαγωγής χαρακτηριστικών (β) χρήση κατάλληλων μηχανισμών μείωσης των διαστάσεων των δεδομένων, οι οποίοι στοχεύουν στην εύρεση συσχετισμών στα δεδομένα με σκοπό την απομάκρυνση μη κρίσιμων πληροφορίων, (γ) εφαρμογή μεθόδων ταξινόμησης που είναι σε θέση να προβλέψουν τις επιθυμητές ετικέτες που σχετίζονται με την κίνηση (αριστερό χέρι, δεξί χέρι, και τα δύο πόδια, γλώσσα), (δ) αντιστοίχηση των προβλεπόμενων σχετικά με την οδήγηση ετικετών σε πραγματικές κινήσεις (στροφή αριστερά, στροφή δεξιά, αύξηση ταχύτητας, μείωση ταχύτητας) και (ε) ενσωμάτωση των καλύτερων μοντέλων, με τη χρήση της μεθόδου ψηφοφορίας, σε ένα τελικό σύστημα BCV.Due to the rapid development of technology, the Human Brain and Computers are interfered with by Bio-Electronic devices employing bio-signals, which are detected by a particular class of sensors called bio-sensors. A new emerging research, the study of bio-signals has focused particularly on mind-controlled technology. More specifically, directly controlling a vehicle using brain waves might assist people with impairments regain their driving abilities as well as offer a fresh option for healthy people to operate a vehicle. The current thesis describes a Brain Controlled Vehicle (BCV) that uses Brain Computer Interface (BCI) technology to interpret Electroencephalography (EEG) data, operate a device, and evaluate brain waves, in order to stay as close as possible to the human nature. The system, which is based on Machine Learning techniques, comprises the following features: (a) Processing of EEG data in order to perform various feature extraction methods; (b) make use of a proper dimensionality reduction method that will find correlations in the data and discard non-critical information; (c) implement classification methods that are able to predict the desired motion related labels (left hand, right hand, both feet, tongue); (d) map the predicted motion related labels into real motions (turn left, turn right, accelerate, slow down) and (e) integrate the best models, with the use of a voting method, into a final BCV system

Study of non-invasive cognitive tasks and feature extraction techniques for brain-computer interface (BCI) applications

A brain-computer interface (BCI) provides an important alternative for disabled people that enables the non-muscular communication pathway among individual thoughts and different assistive appliances. A BCI technology essentially consists of data acquisition, pre-processing, feature extraction, classification and device command. Indeed, despite the valuable and promising achievements already obtained in every component of BCI, the BCI field is still a relatively young research field and there is still much to do in order to make BCI become a mature technology. To mitigate the impediments concerning BCI, the study of cognitive task together with the EEG feature and classification framework have been investigated. There are four distinct experiments have been conducted to determine the optimum solution to those specific issues. In the first experiment, three cognitive tasks namely quick math solving, relaxed and playing games have been investigated. The features have been extracted using power spectral density (PSD), logenergy entropy, and spectral centroid and the extracted feature has been classified through the support vector machine (SVM), K-nearest neighbor (K-NN), and linear discriminant analysis (LDA). In this experiment, the best classification accuracy for single channel and five channel datasets were 86% and 91.66% respectively that have been obtained by the PSD-SVM approach. The wink based facial expressions namely left wink, right wink and no wink have been studied through fast Fourier transform (FFT) and sample range feature and then the extracted features have been classified using SVM, K-NN, and LDA. The best accuracy (98.6%) has been achieved by the sample range-SVM based approach. The eye blinking based facial expression has been investigated following the same methodology as the study of wink based facial expression. Moreover, the peak detection approach has also been employed to compute the number of blinks. The optimum accuracy of 99% has been achieved using the peak detection approach. Additionally, twoclass motor imagery hand movement has been classified using SVM, K-NN, and LDA where the feature has been extracted through PSD, spectral centroid and continuous wavelet transform (CWT). The optimum 74.7% accuracy has been achieved by the PSDSVM approach. Finally, two device command prototypes have been designed to translate the classifier output. One prototype can translate four types of cognitive tasks in terms of 5 watts four different colored bulbs, whereas, another prototype may able to control DC motor utilizing cognitive tasks. This study has delineated the implementation of every BCI component to facilitate the application of brainwave assisted assistive appliances. Finally, this thesis comes to the end by drawing the future direction regarding the current issues of BCI technology and these directions may significantly enhance usability for the implementation of commercial applications not only for the disabled but also for a significant number of healthy users

A supervised machine-learning method for detecting steady-state visually evoked potentials for use in brain computer interfaces: A comparative assessment

It is hypothesised that supervised machine learning on the estimated parameters output by a model for visually evoked potentials (VEPs), created by Kremlácek et al. (2002), could be used to classify steady-state visually evoked potentials (SSVEP) by frequency of stimulation. Classification of SSVEPs by stimulus frequency has application in SSVEP-based brain computer interfaces (BCI), where users are presented with flashing stimuli and user intent is decoded by identifying which stimulus the subject is attending to. We investigate the ability of the model of VEPs to fit the initial portions of SSVEPs, which are not yet in a steady state and contain characteristic features of VEPs superimposed with those of a steady state response. In this process the estimated parameters, as a function of the model for a given SSVEP response, were found. These estimated parameters were used to train several support vector machines (SVM) to classify the SSVEPs. Three initialisation conditions for the model are examined for their contribution to the goodness of fit and the subsequent classification accuracy, of the SVMs. It was found that the model was able to fit SSVEPs with a normalised root mean square error (NRMSE) of 27%, this performance did not match the expected NRMSE values of 13% reported by Kremlácek et al. (2002) for fits on VEPs. The fit data was assessed by the machine learning scheme and generated parameters which were classifiable by SVM above a random chance of 14% (Reang 9% to 28%). It was also shown that the selection of initial parameters had no distinct effect on the classification accuracy. Traditional classification approaches using spectral techniques such as Power Spectral Density Analysis (PSDA) and canonical correlation analysis (CCA) require a window period of data above 1 s to perform accurately enough for use in BCIs. The larger the window period of SSVEP data used the more the Information transfer rate (ITR) decreases. Undertaking a successful classification on only the initial 250 ms portions of SSVEP data would lead to an improved ITR and a BCI which is faster to use. Classification of each method was assessed at three SSVEP window periods (0.25, 0.5 and 1 s). Comparison of the three methods revealed that, on a whole CCA outperformed both the PSDA and SVM methods. While PSDA performance was in-line with that of the SVM method. All methods performed poorly at the window period of 0.25 s with an average accuracy converging on random chance - 14%. At the window period of 0.5 s the CCA only marginally outperformed the SVM method and at a time of 1 s the CCA method significantly (p<0.05) outperformed the SVM method. While the SVMs tended to improve with window period the results were not generally significant. It was found that certain SVMs (Representing a unique combination of subject, initial conditions and window period) achieved an accuracy as high as 30%. For a few instances the accuracy was comparable to the CCA method with a significance of 5%. While we were unable to predict which SVM would perform well for a given subject, it was demonstrated that with further refinement this novel method may produce results similar to or better than that of CCA

Music Recommendation System based on EEG Sentiment Analysis using ML Techniques

Με την πάροδο των χρόνων, πολυάριθμες μελέτες έχουν δείξει ότι η μουσική μπορεί να παράγει ξεχωριστά αποτελέσματα και συναισθήματα στους ανθρώπους. Παρόλο που είναι σχετικά εύκολο να ονομαστούν διαφορετικοί τύποι συναισθημάτων, είναι δύσκολο να συσχετιστούν με τα πραγματικά συναισθήματα που βιώνει κάποιος. Επιπλέον, υπάρχει πληθώρα ανθρώπων που ακούν ένα συγκεκριμένο είδος μουσικής που θεωρούν ευχάριστο όταν στην πραγματικότητα αυτό το είδος μπορεί να έχει αρνητικό αντίκτυπο στους ίδιους. Απώτερος σκοπός της τρέχουσας διπλωματικής είναι η ανάπτυξη ενός συστήματος συστάσεων μουσικής βασιζόμενο σε συναισθήματα που εξάγονται από δεδομένα ηλεκτροεγκεφαλογράφου (EEG), ώστε να παραμείνουν όσο το δυνατόν πιο κοντά στην ανθρώπινη φύση. Το σύστημα αυτό, βασίζεται στις τεχνικές μάθησης μηχανών και περιλαμβάνει τα ακόλουθα χαρακτηριστικά: (α) Επεξεργασία δεδομένων EEG για την ανάπτυξη διαφόρων μεθόδων εξαγωγής χαρακτηριστικών (β) εφαρμογή μεθόδων για αύξηση των δεδομένων ώστε να εμπλουτιστεί το τρέχον σύνολο τους, (γ) χρήση κατάλληλων μηχανισμών μείωσης των διαστάσεων των δεδομένων, οι οποίοι στοχεύουν στην εύρεση συσχετισμών στα δεδομένα με σκοπό την απομάκρυνση μη κρίσιμων πληροφορίων, (δ) εφαρμογή μεθόδων ταξινόμησης που είναι σε θέση να προβλέπουν τις σχετικές με το συναίσθημα ετικέτες (valence, arousal, dominance, liking), (ε) αντιστοίχηση των προβλεπόμενων σχετικά με το συναίσθημα ετικετών σε πραγματικά συναισθήματα (excited, happy, angry, sad) και (στ) ενσωμάτωση των καλύτερων μοντέλων με τη μέθοδο της ψηφοφορίας σε ένα τελικό σύστημα συστάσεων μουσικής.Over the years, numerous studies have demonstrated that music can produce distinct effects and feelings on people. Although it is relatively easy to name different types of emotions, it remains difficult to relate them to the real emotions experienced by a person. In addition, there are many people who listen to a specific genre of music that they think it is enjoyable when in fact that genre might have a negative effect on them. The current thesis, will try to develop a music recommendation system that will base its output on emotions extracted from Electroencephalography (EEG) data so as to stay as close as possible to the human nature. The system, which is based on Machine Learning techniques, comprises the following features: (a) Processing of EEG data in order to perform various feature extraction methods; (b) perform data augmentation so as to enrich the current dataset; (c) make use of a proper dimensionality reduction method that will find correlations in the data and discard non-critical information; (d) implement classification methods that are able to predict emotion related labels (valence, arousal, dominance, liking); (e) map the predicted emotion related labels into real emotions (excited, happy, angry, sad) and (f) integrate the best models, with the use of a voting method, into a final music recommendation system

Novas tecnologias aplicadas à saúde : desenvolvimento de sistemas dinâmicos : conceitos, aplicações e utilização de técnicas inteligentes e regulação

Faculdade UnB Gama (FGA