Pattern recognition (psychology)

Support vector machine

Psychiatry

Artificial intelligence

Psychology

Brain–computer interface

Principal component analysis

Computer science

Component analysis

Independent component analysis

Speech recognition

Electroencephalography

Linear discriminant analysis

IEEE Transactions on Neural Systems and Rehabilitation Engineering

A brain-computer interface (BCI) provides a direct communication channel between a brain and an external device. Steady-state visual evoked potential based BCI (SSVEP-BCI) has received increasing attention due to its high information transfer rate, which is accomplished by individual calibration for frequency recognition. Task-related component analysis (TRCA) is a recent and state-of-the-art method for individually calibrated SSVEP-BCIs. However, in TRCA, the spatial filter learned from each stimulus may be redundant and temporal information is not fully utilized. To address this issue, this paper proposes a novel method, i.e., task-discriminant component analysis (TDCA), to further improve the performance of individually-calibrated SSVEP-BCI. The performance of TDCA was evaluated by two publicly available benchmark datasets, and the results demonstrated that TDCA outperformed ensemble TRCA and other competing methods by a significant margin. An offline and online experiment testing 12 subjects further validated the effectiveness of TDCA. The present study provides a new perspective for designing decoding methods in individually calibrated SSVEP-BCI and presents insight for its implementation in high-speed brain speller applications.

Improving the Performance of Individually Calibrated SSVEP-BCI by Task- Discriminant Component Analysis