Jiabei Tang

Objective: The auditory event-related potential based brain–computer interface (aERP-BCI) is a classical paradigm of brain–computer communication. To improve the coding efficiency of aERP-BCI, this study proposes a method using two parallel voice channels to add the coding dimension based on the cocktail party effect. Methods: The novel paradigm used male and female voices to establish two parallel oddball sound stimulus sequences. In comparison, the baseline paradigm only presented male or female stimulus sequences. Both the double voice condition (DVC) and the single voice condition (SVC) paradigms carried out offline experiments and the DVC also carried out online experiment. Subsequently, the EEG signal and BCI operation results were compared and analyzed. Conclusion: The cocktail party effect caused a significant difference in the EEG responses of non-target stimulus between the focused vocal channel and the ignored vocal channel under the DVC paradigm, and the focused and ignored channels achieved a recognition accuracy of 97.2%. The target recognition rate of DVC was 82.3%, with no significant difference compared with 85% of SVC while the information transfer rate (ITR) of DVC reaching 15.3 bits/min was significantly higher than that of SVC. Significance: The cocktail party effect improves the coding efficiency by adding parallel channels without reducing the target/non-target stimulus recognition in the focused vocal channel. This provides a novel direction for the performance improvement of aERP-BCI.

The brain–computer interface (BCI) spellers based on steady-state visual evoked potentials (SSVEPs) have recently been widely investigated for their high information transfer rates (ITRs). This paper aims to improve the practicability of the SSVEP-BCIs for high-speed spelling. The system acquired the electroencephalogram (EEG) data from a self-developed dedicated EEG device and the stimulation was arranged as a keyboard. The task-related component analysis (TRCA) spatial filter was modified (mTRCA) for target classification and showed significantly higher performance compared with the original TRCA in the offline analysis. In the online system, the dynamic stopping (DS) strategy based on Bayesian posterior probability was utilized to realize alterable stimulating time. In addition, the temporal filtering process and the programs were optimized to facilitate the online DS operation. Notably, the online ITR reached 330.4 ± 45.4 bits/min on average, which is significantly higher than that of fixed stopping (FS) strategy, and the peak value of 420.2 bits/min is the highest online spelling ITR with a SSVEP-BCI up to now. The proposed system with portable EEG acquisition, friendly interaction, and alterable time of command output provides more flexibility for SSVEP-based BCIs and is promising for practical high-speed spelling.

Multifocal steady-state visual evoked potentials (mfSSVEPs) have been successfully applied to assess visual field loss in glaucoma. However, the potential of mfSSVEPs for command control has not been fully explored yet. It is significant to detect single-trial mfSSVEPs and establish a brain-computer interface (BCI) system. This study designed a stimulating paradigm that contains 32 targets, with each target composed of five fan-shaped flickers in a circle. The five flickers were modulated by five frequencies and formed a five-bit binary encoding system through controlling the ON/OFF state of each flicker. Twelve subjects participated in an offline and an online experiments. Inter-task-related component analysis (iTRCA) combined with a probabilistic model was proposed for target recognition. Notably, the training data needed for calibration corresponded to only six out of the 32 targets. It was found that the increasing number of flickers showed a negative impact on the mfSSVEP signal. The accuracy reached 80.9% ± 11.7% on average with a peak of 95.3% by iTRCA, which was significantly higher than that by a traditional method. The results indicate that the proposed stimulation and algorithm are effective for encoding and decoding BCI commands. Therefore, the mfSSVEP-based BCI enables the augmentation of the BCI instruction set without any burden of collecting extra training data.