Abstract:
A versatile Brain Computer Interface (BCI) system is designed and implemented to assist people with severe disabilities in achieving a fair level of autonomy. The versatility of the proposed BCI system lies in the fact that it can be custom-tailored to individual users while not only mitigating deleterious artefacts, but also putting them to an advantage for an asynchronous, interactive, real-time, and fault-tolerant assistive system. Independent Component Analysis (ICA) and correlation-based Template Matching (TM) are integrated in a novel way in order to detect and intelligently handle artefacts. Hence, this BCI differentiates between involuntary eye blinks (considered artefacts, thus removed) and deliberate rapid eye blinks (considered synchronizing signals) used for distress calling, start/stop signalling, as well as fault-tolerance owing to the confirmation/cancellation of commands before their execution. Two classes of brain activities, optimized to suit the capabilities of each patient, are used to navigate through a menu of commands intended to individually meet the users’ needs. The Wavelet Transform (WT) is used to extract sub-band-power-based features that are input to a Neural Network used as the classifier with a success rate reaching 90%. The system can flexibly be adapted to suit various scenarios involving binary load control (on/off of TV, light, A/C, etc…) as well as multilevel control (up/down level of bed, TV volume, room temperature…etc.). The merits of this system have been successfully demonstrated in practice, showing its potential contribution to smart hospitals and patient-care facilities.
