Amplitude Modulation Depth Coding Method for SSVEP-Based Brain–Computer Interfaces

Abstract

In steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs), the limited availability of frequency resources inherently constrains the scale of the instruction set, presenting a substantial challenge for efficient communication. As the number of stimuli increases, the comfort level of the stimulus interface also becomes increasingly demanding due to the expanded flickering area. To address these issues, we proposed a novel amplitude modulation depth coding (AMDC) method that employs Amplitude Shift Keying (ASK) technique to modulate the luminance level of stimuli dynamically. Each stimulus with a single carrier frequency was assigned a specific binary sequence to operate two modulation depths. Two experiments were conducted to comprehensively assess the effectiveness of this approach. In Experiment 1, the time-frequency responses at two modulation depths across different frequencies were examined. A 36-target paradigm based on AMDC strategy was designed and evaluated in terms of user experience and classification performance in Experiment 2. The results show that the proposed paradigm obtains an average classification accuracy of $81.7~\pm ~12.6$ % with an average information transfer rate (ITR) of $45.4~\pm ~11.5$ bits/min. Moreover, it significantly reduces flicker perception and improves comfort level compared to traditional SSVEP stimuli with uniform modulation depth. Given its capability to improve coding efficiency for a single frequency and improve user experience, this method shows promising potential for application in large-scale command SSVEP-based BCI systems.

Keywords

• Brain-computer interfaces
• electroencephalography
• steady-state visual evoked potentials
• amplitude modulation depth coding