Decoding of top-down cognitive processing for SSVEP-controlled BMI.
We present a fast and accurate non-invasive brain-machine interface (BMI) based on demodulating steady-state visual evoked potentials (SSVEPs) in electroencephalography (EEG). Our study reports an SSVEP-BMI that, for the first time, decodes primarily based on top-down and not bottom-up visual information processing. The experimental setup presents a grid-shaped flickering line array that the participants observe while intentionally attending to a subset of flickering lines representing the shape of a letter. While the flickering pixels stimulate the participant's visual cortex uniformly with equal probability, the participant's intention groups the strokes and thus perceives a 'letter Gestalt'. We observed decoding accuracy of 35.81% (up to 65.83%) with a regularized linear discriminant analysis; on average 2.05-fold, and up to 3.77-fold greater than chance levels in multi-class classification. Compared to the EEG signals, an electrooculogram (EOG) did not significantly contribute to decoding accuracies. Further analysis reveals that the top-down SSVEP paradigm shows the most focalised activation pattern around occipital visual areas; Granger causality analysis consistently revealed prefrontal top-down control over early visual processing. Taken together, the present paradigm provides the first neurophysiological evidence for the top-down SSVEP BMI paradigm, which potentially enables multi-class intentional control of EEG-BMIs without using gaze-shifting.
Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea.
Byoung-Kyong
BK
Min
Machine Learning Group, Berlin Institute of Technology, Berlin 10587, Germany.
Sven
S
Dähne
Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea.
Min-Hee
MH
Ahn
Department of Mechanical and Aerospace Engineering, Seoul National University, Seoul 08826, Republic of Korea.
Yung-Kyun
YK
Noh
Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea.|Machine Learning Group, Berlin Institute of Technology, Berlin 10587, Germany.
Klaus-Robert
KR
Müller
D000328:Adult; D062207:Brain-Computer Interfaces; D003071:Cognition / Q000502:physiology*; D004569:Electroencephalography / Q000379:methods; D005074:Evoked Potentials, Visual / Q000502:physiology*; D005260:Female; D005425:Flicker Fusion / Q000502:physiology; D006801:Humans; D008297:Male; D010775:Photic Stimulation / Q000379:methods; D013647:Task Performance and Analysis; D014793:Visual Cortex / Q000502:physiology*