A comparison of univariate, vector, bilinear autoregressive, and band power features for brain–computer interfaces

Selecting suitable feature types is crucial to obtain good overall brain–computer interface performance. Popular feature types include logarithmic band power (logBP), autoregressive (AR) parameters, time-domain parameters, and wavelet-based methods. In this study, we focused on different variants of AR models and compare performance with logBP features. In particular, we analyzed univariate, vector, and bilinear AR models. We used four-class motor imagery data from nine healthy users over two sessions. We used the first session to optimize parameters such as model order and frequency bands. We then evaluated optimized feature extraction methods on the unseen second session. We found that band power yields significantly higher classification accuracies than AR methods. However, we did not update the bias of the classifiers for the second session in our analysis procedure. When updating the bias at the beginning of a new session, we found no significant differences between all methods anymore. Furthermore, our results indicate that subject-specific optimization is not better than globally optimized parameters. The comparison within the AR methods showed that the vector model is significantly better than both univariate and bilinear variants. Finally, adding the prediction error variance to the feature space significantly improved classification results

Comparison of three methods for adapting LDA classifiers with BCI applications

Due to the non-stationarity of electroencephalogram (EEG) signals, online training and adaptation is essential to EEG based brain-computer interface (BCI) systems. Three methods were used to adapt linear discriminant analysis (LDA) classifiers during simulated online training for a comparative study. One method generates a new classifier based on updated means and variances of the BCI data of different classes, and the other two are Kalman filter and extended Kalman filter based methods that adapt LDA's parameters directly. Cue-based motor imagery BCI experiments were carried out with 9 naive subjects. Results show that all methods returned comparable improvement during online training, but the mean-variance updating based method is much simpler than the other two methods

Unsupervised movement onset detection from EEG recorded during self-paced real hand movement

This article presents an unsupervised method for movement onset detection from electroencephalography (EEG) signals recorded during self-paced real hand movement. A Gaussian Mixture Model (GMM) is used to model the movement and idle-related EEG data. The GMM built along with appropriate classification and post processing methods are used to detect movement onsets using self-paced EEG signals recorded from five subjects, achieving True-False rate difference between 63 and 98%. The results show significant performance enhancement using the proposed unsupervised method, both in the sample-by-sample classification accuracy and the event-by-event performance, in comparison with the state-of-the-art supervised methods. The effectiveness of the proposed method suggests its potential application in self-paced Brain-Computer Interfaces (BCI). © 2009 International Federation for Medical and Biological Engineering

Brain-Controlled Wheelchair Through Discrimination of Two Mental Tasks

Recently, Brain-Computer Interface (BCI) research has been targeted at the rehabilitation of motor-disabled individuals because it helps to establish a communication and control channel for them. This new channel could be used to restore motor functions or to provide them with mobility using a BCI controlled motorized wheelchair. One of the most important limitations of these systems is to guarantee that a person can, through his mental activity, safely control the variety of navigation commands that provide control of the wheelchair: advance, turn, move back, and stop. The vast majority of the mobile robot navigation applications that are controlled via a BCI demand that the user performs as many different mental tasks as there are different control commands. Having a higher number of commands makes it easier for the subjects to navigate through the environment, since they have more choices to move. However, despite this is an intuitive solution, the classification accuracy of such systems gets worse as the number of mental tasks to identify increases. Some studies proved that the best classification accuracy is achieved when only two classes are discriminated. In order to enable an effective and autonomous wheelchair navigation with a BCI system without worsening user performance, our group proposed and later developed a new paradigm based on the discrimination of only two classes (one active mental task versus any other mental activity), which enabled the selection of four commands, besides the stop command: move forwards, turn right, move backward and turn left. In the present study, a subject participated in an experiment in order to freely control a wheelchair carrying out continuous movements. The obtained results suggest that the proposed BCI system seems to be an effective way of driving a robotic wheelchair autonomously.This work was partially supported by the University of Málaga, by the Spanish Ministry of Economy and Competitiveness through the projects LICOM (DPI2015-67064-R) and INCADI (TEC 2011-26395), and by the European Regional Development Fund (ERDF).2018-12-3