Real-time brain computer interface using imaginary movements

Abstract

Background: Brain Computer Interface (BCI) is the method of transforming mental thoughts and imagination into actions. A real-time BCI system can improve the quality of life of patients with severe neuromuscular disorders by enabling them to communicate with the outside world. In this paper, the implementation of a 2-class real-time BCI system based on the event related desynchronization (ERD) of the sensorimotor rhythms (SMR) is described. Methods: Off-line measurements were conducted on 12 healthy test subjects with 3 different feedback systems (cross, basket and bars). From the collected electroencephalogram (EEG) data, the optimum frequency bands for each of the subjects were determined first through an exhaustive search on 325 bandpass filters. The features were then extracted for the left and right hand imaginary movements using the Common Spatial Pattern (CSP) method. Subsequently, a Bayes linear classifier (BLC) was developed and used for signal classification. These three subject-specific settings were preserved for the on-line experiments with the same feedback systems. Results: Six of the 12 subjects were qualified for the on-line experiments based on their high off-line classification accuracies (CAs > 75 %). The overall mean on-line accuracy was found to be 80%. Conclusions:The subject-specific settings applied on the feedback systems have resulted in the development of a successful real-time BCI system with high accuracies.

Vocal Imagery vs Intention: Viability of Vocal-Based EEG-BCI Paradigms

“…Imagery has been shown to be easy to study with a variety of modalities, such as functional near-infrared spectroscopy (fNIRS) [7], functional magnetic resonance imaging (fMRI) [8], magnetoencephalography (MEG) [9] and EEG [10]. The comparatively low complexity to study and classify imagery with techniques such as EEG, has opened for research in BCIs utilizing imagery as the sensory input (the brain signals) [11].…”