Estimating and approaching the maximum information rate of noninvasive visual brain-computer interface

Intermodulation frequency components in steady-state visual evoked potentials: Generation, characteristics and applications

The steady-state visual evoked potentials (SSVEPs), evoked by dual-frequency or multi-frequency stimulation, likely contains intermodulation frequency components (IMs). Visual IMs are products of nonlinear integration of neural signals and can be evoked by various paradigms that induce neural interaction. IMs have demonstrated many interesting and important characteristics in cognitive psychology, clinical neuroscience, brain-computer interface and other fields, and possess substantial research potential. In this paper, we first review the definition of IMs and summarize the stimulation paradigms capable of inducing them, along with the possible neural origins of IMs. Subsequently, we describe the characteristics and derived applications of IMs in previous studies, and then introduced three signal processing methods favored by researchers to enhance the signal-to-noise ratio of IMs. Finally, we summarize the characteristics of IMs, and propose several potential future research directions related to IMs.

Visual tracking brain-computer interface

Brain-computer interfaces (BCIs) offer a way to interact with computers without relying on physical movements. Non-invasive electroencephalography-based visual BCIs, known for efficient speed and calibration ease, face limitations in continuous tasks due to discrete stimulus design and decoding methods. To achieve continuous control, we implemented a novel spatial encoding stimulus paradigm and devised a corresponding projection method to enable continuous modulation of decoded velocity. Subsequently, we conducted experiments involving 17 participants and achieved Fitt's information transfer rate (ITR) of 0.55 bps for the fixed tracking task and 0.37 bps for the random tracking task. The proposed BCI with a high Fitt's ITR was then integrated into two applications, including painting and gaming. In conclusion, this study proposed a visual BCI based-control method to go beyond discrete commands, allowing natural continuous control based on neural activity.