A Self-Paced Two-State Mental Task-Based Brain-Computer Interface with Few EEG Channels

A self-paced brain-computer interface (BCI) system that is activated by mental tasks is introduced. The BCI’s output has two operational states, the active state and the inactive state, and is activated by designated mental tasks performed by the user. The BCI could be operated using several EEG brain electrodes (channels) or only few (i.e., five or seven channels) at a small loss in performance. The performance is evaluated on a dataset we have collected from four subjects while performing one of the four different mental tasks. The dataset contains the signals of 29 EEG electrodes distributed over the scalp. The five and seven highly discriminatory channels are selected using two different methods proposed in the paper. The signal processing structure of the interface is computationally simple. The features used are the scalar autoregressive coefficients. Classification is based on the quadratic discriminant analysis. Model selection and testing procedures are accomplished via cross-validation. The results are highly promising in terms of the rates of false and true positives. The false-positive rates reach zero, while the true-positive rates are sufficiently high, i.e., 54.60 and 59.98% for the 5-channel and 7-channel systems, respectively

Design of a self-paced brain-computer interface based on mental tasks

People with very severe motor-disabilities have to accept a much-reduced quality of life. Unfortunately, these people cannot use assistive devices as present devices require motor activities. Brain-computer interfaces (BCIs) provide an alternative means of communication between the brain and assistive devices. There are two types of BCIs, synchronous and self-paced. Self-paced BCIs are more practical as they can be used at any time. The vast majority of existing self-paced BCIs are activated by real, attempted, or imagined movements. Few are activated by mental tasks. The high false positive rates (FPRs) of existing self-paced BCIs render them impractical. There are no self-paced BCIs based on motor movements that have low FPRs. However, self-paced BCIs with low FPRs based on mental tasks have been proposed. Designing a self-paced mental task-based BCI with a zero or near zero FPR and a reasonable true positive rate (TPR) is the goal of this thesis. We investigated the feasibility of having a self-paced mental task-based BCI with a zero or near zero FPR. The EEG signals from 6 electrodes of 4 subjects performing 4 mental tasks are used. Features were extracted using autoregressive modeling. Different classifiers were tested. The results were promising in that zero FPRs were obtained. The data used, however, had not been collected in a self-paced paradigm. We then collected the EEG signals from 29 channels of 4 subjects performing 4 mental tasks in a self-paced paradigm. We evaluated the performance of our BCI using this dataset. It yielded an average TPR of 67.26% and zero FPR. To make the system practical, we decrease the number of channels from 29 to 7 and 5, using 2 approaches that yield local optimal results. The average TPR is sufficiently high (54.60% and 59.98% for systems with 5 and 7 channels) while the FPRs remain zero. We also study the effects on the performance, as the segment length is varied. For the 7-channel BCIs, the optimum length is between 1 and 2.5 sec. The average TPR is improved to 63.47%. The FPRs are zero. We also show that our BCIs are robust to artifacts.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat