Classification of EEG Signals Using Dempster Shafer Theory and a K-Nearest Neighbor Classiﬁer

A brain computer interface (BCI) is a communication system, which translates brain activity into commands for a computer or other devices. Nearly all BCIs contain as a core component a classification algorithm, which is employed to discriminate different brain activities using previously recorded examples of brain activity. In this paper, we study the classification accuracy achievable with a k-nearest neighbor (KNN) method based on Dempster- Shafer theory. To extract features from the electroencephalogram (EEG) signals, autoregressive (AR) models and wavelet decomposition are used. To test the classification method an EEG dataset containing signals recorded during the performance of five different mental tasks is used. We show that the Dempster-Shafer KNN classifier achieves a higher correct classification rate than the classical voting KNN classifier and the distance- weighted KNN classifier.

An efficient P300-based brain-computer interface for disabled subjects

Touradj Ebrahimi, Ulrich Hoffmann, Jean-Marc Vesin

A brain-computer interface (BCI) is a communication system that translates brain-activity into commands for a computer or other devices. In other words, a BCI allows users to act on their environment by using only brain-activity, without using peripheral nerves and muscles. In this paper, we present a BCI that achieves high classification accuracy and high bitrates for both disabled and able-bodied subjects. The system is based on the P300 evoked potential and is tested with five severely disabled and four able-bodied subjects. For four of the disabled subjects classification accuracies of 100% are obtained. The bitrates obtained for the disabled subjects range between 10 and 25 bits/min. The effect of different electrode configurations and machine learning algorithms on classification accuracy is tested. Further factors that are possibly important for obtaining good classification accuracy in P300-based BCI systems for disabled subjects are discussed.

2008

Recent Advances in Brain-Computer Interfaces

Touradj Ebrahimi, Ulrich Hoffmann, Jean-Marc Vesin

A brain-computer interface (BCI) is a communication system that translates brain activity into commands for a computer or other devices. In other words, a BCI allows users to act on their environment by using only brain activity, without using peripheral nerves and muscles. The major goal of BCI research is to develop systems that allow disabled users to communicate with other persons, to control artificial limbs, or to control their environment. An alternative application area for brain-computer interfaces (BCIs) lies in the field of multimedia communication. To develop systems for usage in the field of assistive technology or multimedia communication, many aspects of BCI systems are currently being investigated. Research areas include evaluation of invasive and noninvasive technologies to measure brain activity, evaluation of control signals (i.e. patterns of brain activity that can be used for communication), development of algorithms for translation of brain signals into computer commands, and the development of new BCI applications. In this paper we give an introduction to some of the aspects of BCI research mentioned above, present a concrete example of a BCI system, and highlight recent developments and open problems.

2007

Brain-Computer Interface in Multimedia Systems

Ashkan Yazdani

A brain-computer interface (BCI) is a system that allows a user to communicate with the environment only through cerebral activity, without using muscular output channels. To establish a direct link between the brain and a computer, the electroencephalogram (EEG) signal of a user is measured and then analyzed with the help of signal processing and machine learning algorithms. Once a certain mental activity has been detected by the computer, a response can be displayed on a screen or a command can be sent to a peripheral device, for example a wheelchair or a television. The main application area for BCI is assistive technology for handicapped people. For example, one can imagine artificial limbs controlled by a BCI, a BCI-based spelling device, or an environment control system based on a BCI. Nevertheless, during the last years it has been convincingly shown that communication via a BCI is feasible for able-bodied as well as handicapped users and several new applications such as entertainment and gaming, and neuro-feedback have been proposed for BCI systems. This thesis describes the author’s contributions to the research on brain computer interface systems, focusing on exploring and studying new applications of BCIs especially in multimedia communication domain. After providing a general overview of the EEG signal processing techniques and current BCI systems, three topics of interest have been identified. These topics include BCI for salient image retrieval and image triage, BCI for affect recognition during multimedia consumption, and BCI for studying the EEG correlates of pleasant and unpleasant odors. In the first topic, we present a BCI system, capable of identifying interesting images in an image database. Using this system, images of a database are presented to several users at a fast rate, and the information on whether a given image is salient, is implicitly extracted by means of processing the EEG signals, acquired when users are watching the image sequence. Furthermore, in order to investigate the impact of expertise on a BCI-based salient image retrieval system, the changes in the EEG signals of expert and novice users (with respect to image content) are studied. We show that it is possible to define experimental protocol and judiciously apply signal processing tools such that a practical BCI system can be set up for detection of interest in users during the watching of image sequences. Furthermore, we show that a relatively high retrieval accuracy can be obtained for most of the users of this system. The second presented topic deals with an affective BCI system, which recognizes the emotional states of users While they watch different music video clips. More precisely, the users are asked to watch several emotive music video clips, while their EEG and other peripheral physiological signals are acquired. Signal processing and machine learning algorithms are then developed to infer the emotional state of the user, induced while watching the music video clips. We propose the application of this BCI system in implicit emotional annotation of multimedia contents and sketch a iii iv road map towards developing a hybrid BCI system for emotional search and retrieval of multimedia contents. In the last topic, we present the results of our study on exploring the EEG alterations during the perception of pleasant and unpleasant odorant stimuli. We identify the regions of the brain cortex, which contribute to differentiation of pleasant and unpleasant odors and show that a relatively high accuracy for classification of EEG signals during perception of hedonically different odorant stimuli can be achieved. Furthermore, we identify a novel clinical application for the developed system, namely to study the EEG changes in comatose patients during stimulation with hedonically different odors in order to estimate the depth of coma.

EPFL2012

Brain-Computer Interface in Multimedia Systems

Ashkan Yazdani

EPFL2012