Abstract
This study implemented a systematic user-centered training protocol for a 4-class brain-computer interface (BCI). The goal was to optimize the BCI individually in order to achieve high performance within few sessions for all users. Eight able-bodied volunteers, who were initially naïve to the use of a BCI, participated in 10 sessions over a period of about 5 weeks. In an initial screening session, users were asked to perform the following seven mental tasks while multi-channel EEG was recorded: mental rotation, word association, auditory imagery, mental subtraction, spatial navigation, motor imagery of the left hand and motor imagery of both feet. Out of these seven mental tasks, the best 4-class combination as well as most reactive frequency band (between 8-30 Hz) was selected individually for online control. Classification was based on common spatial patterns and Fisher’s linear discriminant analysis. The number and time of classifier updates varied individually. Selection speed was increased by reducing trial length. To minimize differences in brain activity between sessions with and without feedback, sham feedback was provided in the screening and calibration runs in which usually no real-time feedback is shown. Selected task combinations and frequency ranges differed between users. The tasks that were included in the 4-class combination most often were (1) motor imagery of the left hand (2), one brain-teaser task (word association or mental subtraction) (3), mental rotation task and (4) one more dynamic imagery task (auditory imagery, spatial navigation, imagery of the feet). Participants achieved mean performances over sessions of 44-84% and peak performances in single-sessions of 58-93% in this user-centered 4-class BCI protocol. This protocol is highly adjustable to individual users and thus could increase the percentage of users who can gain and maintain BCI control. A high priority for future work is to examine this protocol with severely disabled users.
Highlights
A brain-computer interface (BCI) translates physiological brain signals into an output that reflects the user’s intent
Online performance The results confirmed that selected task combinations and frequency ranges differed between users
That for each user one motor task and one brain-teaser task was included in the task combination
Summary
A brain-computer interface (BCI) translates physiological brain signals into an output that reflects the user’s intent. It can provide severely motor-impaired users with a new, nonmuscular channel for communication and control which may be their only possibility to interact with the external world [1]. One way to implement a BCI involves non-invasively recording the rhythmic activity of the brain’s electrophysiological signals by electroencephalography (EEG) and detecting the amplitude changes (event-related (de)synchronization, ERD/S [7]) that users voluntarily produce. Able-bodied as well as disabled participants used motor imagery tasks (i.e. the kinesthetic mental imagination of movements) to induce characteristic ERD/S patterns Recent studies including able-bodied as well as disabled individuals revealed huge individual differences in best task combinations [18,22]
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