Abstract
How to encode as many targets as possible with a limited-frequency resource is a difficult problem in the practical use of a steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) speller. To solve this problem, this study developed a novel method called dual-frequency biased coding (DFBC) to tag targets in a SSVEP-based 48-character virtual speller, in which each target is encoded with a permutation sequence consisting of two permuted flickering periods that flash at different frequencies. The proposed paradigm was validated by 11 participants in an offline experiment and 7 participants in an online experiment. Three occipital channels (O1, Oz, and O2) were used to obtain the SSVEP signals for identifying the targets. Based on the coding characteristics of the DFBC method, the proposed approach has the ability of self-correction and thus achieves an accuracy of 76.6% and 79.3% for offline and online experiments, respectively, which outperforms the traditional multiple frequencies sequential coding (MFSC) method. This study demonstrates that DFBC is an efficient method for coding a high number of SSVEP targets with a small number of available frequencies.
Highlights
B RAIN-computer interfaces (BCIs) provide a direct communication pathway between the brain and the external environment by translating the brain activity patterns of a user into commands for an interactive application [1]
state visual evoked potential (SSVEP) is widely used in BCI spelling because of its advantages of ease of use, little or no required training, multi-command output, and high signal-to-noise ratio (SNR) together with a high information transmission rate (ITR) [14], [15]
In this study, we propose a dual-frequency biased coding (DFBC) method to extend the traditional multiple frequencies sequential coding (MFSC) method, in which each cycle of the target presentation is divided into two biased flickering periods, and each period flashes at different frequencies
Summary
B RAIN-computer interfaces (BCIs) provide a direct communication pathway between the brain and the external environment by translating the brain activity patterns of a user into commands for an interactive application [1]. SSVEPs are brain electrical signals evoked by visual stimuli that flash at specific frequencies. This neural response consists of oscillatory activity of the base frequency and harmonics of the visual stimulus and is mainly concentrated in the visual cortex, which is located in the occipital region of the brain [13]. Tagging visual stimuli by different frequencies, phases, temporal or spatial patterns of visual flicker and classifying the respective features from SSVEPs enables a BCI system to identify the command selected by the user. SSVEP is widely used in BCI spelling because of its advantages of ease of use, little or no required training, multi-command output, and high signal-to-noise ratio (SNR) together with a high information transmission rate (ITR) [14], [15]
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