Abstract
Brain-Computer Interfaces (BCIs) allow users to control devices and communicate by using brain activity only. BCIs based on broad-band visual stimulation can outperform BCIs using other stimulation paradigms. Visual stimulation with pseudo-random bit-sequences evokes specific Broad-Band Visually Evoked Potentials (BBVEPs) that can be reliably used in BCI for high-speed communication in speller applications. In this study, we report a novel paradigm for a BBVEP-based BCI that utilizes a generative framework to predict responses to broad-band stimulation sequences. In this study we designed a BBVEP-based BCI using modulated Gold codes to mark cells in a visual speller BCI. We defined a linear generative model that decomposes full responses into overlapping single-flash responses. These single-flash responses are used to predict responses to novel stimulation sequences, which in turn serve as templates for classification. The linear generative model explains on average 50% and up to 66% of the variance of responses to both seen and unseen sequences. In an online experiment, 12 participants tested a 6 × 6 matrix speller BCI. On average, an online accuracy of 86% was reached with trial lengths of 3.21 seconds. This corresponds to an Information Transfer Rate of 48 bits per minute (approximately 9 symbols per minute). This study indicates the potential to model and predict responses to broad-band stimulation. These predicted responses are proven to be well-suited as templates for a BBVEP-based BCI, thereby enabling communication and control by brain activity only.
Highlights
A Brain-Computer Interface (BCI) enables one to control a device by using brain activity only, bypassing the peripheral nervous system
We investigate a novel approach to synchronous Broad-Band Visually Evoked Potentials (BBVEPs)-based BCI
In the copy-spelling block, the average classification rate in the fixed-length condition was 86 percent. This yields an Information Transfer Rate (ITR) of 38.12 bits per minute and an Symbols Per Minute (SPM) of 6.93 symbols per minute
Summary
A Brain-Computer Interface (BCI) enables one to control a device by using brain activity only, bypassing the peripheral nervous system. Because BCI is not dependent on muscle control, it provides an additional output channel for the brain to be used for communication and control. The online BCI cycle can be initiated by specific stimulation that evokes characteristic taskrelated brain activity. Brain activity is commonly measured by electroencephalogram (EEG) recordings. A computer interprets the measured EEG following several pre-processing steps (e.g., cleaning the data) and machine learning techniques (e.g., learning task-related and subject-specific brain activity). With this analysis a computer can decode and detect task-related information from brain activity.
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