Toward a hybrid brain-computer interface based on imagined movement and visual attention

Area of Science:

• Neuroscience
• Biomedical Engineering
• Human-Computer Interaction

Background:

• Conventional brain-computer interface (BCI) systems exhibit variable efficacy across users.
• Certain users fail to achieve adequate accuracy for effective communication with existing BCI paradigms.
• Existing BCI approaches, such as event-related desynchronization (ERD) and steady-state visual evoked potential (SSVEP), have limitations for some individuals.

Purpose of the Study:

• To introduce and evaluate a novel hybrid BCI task combining ERD and SSVEP.
• To assess the potential of this hybrid approach to enhance BCI accuracy, particularly for non-optimal users.
• To compare the performance and subjective experience of the hybrid BCI with individual ERD and SSVEP tasks.

Main Methods:

• Subjects performed individual ERD (imagined hand movement), SSVEP (visual stimulus focus), and a hybrid task (simultaneous ERD and SSVEP).
• Electroencephalographic (EEG) data were recorded during these three conditions.
• Offline analyses assessed accuracy and subjective difficulty, identifying user performance across tasks.

Main Results:

• Approximately half of the subjects showed insufficient accuracy with at least one individual BCI task (ERD or SSVEP).
• Switching to a different BCI task improved accuracy for some users.
• The hybrid BCI approach completely resolved accuracy issues for all subjects without increasing perceived difficulty.

Conclusions:

• The hybrid BCI approach combining ERD and SSVEP is validated as a method to overcome accuracy limitations.
• Users experiencing difficulties with conventional BCIs should consider alternative or hybrid paradigms.
• Proficient users may enhance information throughput by combining ERD and SSVEP for improved accuracy and expanded command sets.