Frequency and Phase Mixed Coding in SSVEP-Based Brain--Computer Interface

• Published in: IEEE transactions on biomedical engineering Vol. 58; no. 1; pp. 200 - 206
• Main Authors: Jia, Chuan, Gao, Xiaorong, Hong, Bo, Gao, Shangkai
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.01.2011; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Adult; Algorithms; Applied sciences; Biological and medical sciences; Brain > computer interfaces (BCI); Computer science; control theory; systems; Computer systems and distributed systems. User interface; Electrodiagnosis. Electric activity recording; Electroencephalography; Electroencephalography - methods; Electronics; Evoked Potentials, Visual - physiology; Exact sciences and technology; Fourier Analysis; frequency coding; Frequency measurement; Hardware; Harmonic analysis; Humans; Input-output equipment; Investigative techniques, diagnostic techniques (general aspects); Lead; Man-Machine Systems; Materials; Medical sciences; Nervous system; phase coding; Phase measurement; Signal Processing, Computer-Assisted; Software; steady-state visual evoked potential (SSVEP); Human; phase coding; frequency coding; Electrophysiology; Visual evoked potential; Man machine dialogue; Brain-computer interfaces (BCI); Steady state; steady-state visual evoked potential (SSVEP); Brain-computer interface; User interface; Signal processing; Biomedical engineering
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2010.2068571

Frequency coding has been the traditional method implemented in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCI). However, it is limited in terms of possible target numbers and, consequently, inappropriate for certain applications involving liquid crystal display (LCD) with multiple stimuli. This paper proposes an innovative coding method for SSVEP that, through a combination of frequency and phase, increases the number of targets, thus it improves the information transfer rate (ITR). With this method, a BCI system with 15 targets was developed using three stimulus frequencies, which is five times as many targets as the traditional method. Additionally, this paper defines the concept of reference phase, and decodes the EEG by means of Fourier coefficient projections onto the reference phase directions. Through the optimization of lead position, reference phase, data segment length, and harmonic components, the average ITR exceeded 60 bits/min in a simulated online test with ten subjects.