Recognition of the idle state based on a novel IFB-OCN method for an asynchronous brain-computer interface

Comparison of recognition methods for an asynchronous (un-cued) BCI system: an investigation with 40-class SSVEP dataset

Steady-state visual evoked potential (SSVEP)-based brain-computer Interface (BCI) has demonstrated the potential to manage multi-command targets to achieve high-speed communication. Recent studies on multi-class SSVEP-based BCI have focused on synchronous systems, which rely on predefined time and task indicators; thus, these systems that use passive approaches may be less suitable for practical applications. Asynchronous systems recognize the user's intention (whether or not the user is willing to use systems) from brain activity; then, after recognizing the user's willingness, they begin to operate by switching swiftly for real-time control. Consequently, various methodologies have been proposed to capture the user's intention. However, in-depth investigation of recognition methods in asynchronous BCI system is lacking. Thus, in this work, three recognition methods (power spectral density analysis, canonical correlation analysis (CCA), and support vector machine (SVM)) used widely in asynchronous SSVEP BCI systems were explored to compare their performance. Further, we categorized asynchronous systems into two approaches (1-stage and 2-stage) based upon the recognition process's design, and compared their performance. To do so, a 40-class SSVEP dataset collected from 40 subjects was introduced. Finally, we found that the CCA-based method in the 2-stage approach demonstrated statistically significantly higher performance with a sensitivity of 97.62 ± 02.06%, specificity of 76.50 ± 23.50%, and accuracy of 75.59 ± 10.09%. Thus, it is expected that the 2-stage approach together with CCA-based recognition and FB-CCA classification have good potential to be implemented in practical asynchronous SSVEP BCI systems.

Using oscillatory and aperiodic neural activity features for identifying idle state in SSVEP-based BCIs reduces false triggers

Objective.In existing studies, rhythmic (oscillatory) components were used as main features to identify brain states, such as control and idle states, while non-rhythmic (aperiodic) components were ignored. Recent studies have shown that aperiodic (1/f) activity is functionally related to cognitive processes. It is not clear if aperiodic activity can distinguish brain states in asynchronous brain-computer interfaces (BCIs) to reduce false triggers. In this paper, we propose an asynchronous method based on the fusion of oscillatory and aperiodic features for steady-state visual evoked potential-based BCIs.Approach.The proposed method first evaluates the oscillatory and aperiodic components of control and idle states using irregular-resampling auto-spectral analysis. Oscillatory features are then extracted using the spectral power of fundamental, second-harmonic, and third-harmonic frequencies of the oscillatory component, and aperiodic features are extracted using the slope and intercept of the first-order polynomial of the spectral fit of the aperiodic component under a log-logarithmic axis. The process produces two types of feature pools (oscillatory, aperiodic features). Next, feature selection (dimensionality reduction) is applied to the feature pools by Bonferroni correctedp-values from two-way analysis of variance. Last, these spatial-specific statistically significant features are used as input for classification to identify the idle state.Mainresults.On a 7-target dataset from 15 subjects, the mix of oscillatory and aperiodic features achieved an average accuracy of 88.39% compared to 83.53% when using oscillatory features alone (4.86% improvement). The results demonstrated that the proposed idle state recognition method achieved enhanced performance by incorporating aperiodic features.Significance.Our results demonstrated that (1) aperiodic features were effective in recognizing idle states and (2) fusing features of oscillatory and aperiodic components enhanced classification performance by 4.86% compared to oscillatory features alone.

Comparative study of attention-related features on attention monitoring systems with a single EEG channel

The easy-to-use attention monitoring systems usually detect the participant's attentional status via processing electroencephalogram (EEG) data recorded from a single FPz channel. But due to the influence of noises and artifacts, the attention-monitoring performance needs to be further improved to suit different individuals and devices. This paper compared the attention-related features extracted using four state-of-the-art methods including delta/beta1 (D/B1), α + β + δ + θ + R, entropy and optimized complex network (OCN). The classification performance was evaluated using receiver operating characteristic (ROC) curves and area under the ROC curves (AUC) on two EEG data acquisition devices, i.e., a BrainAmp device with high precision and a Sichiray device with low cost, respectively. Considering the varied performance on different individuals and devices, this paper proposed a novel Mutual information-based feature fusion (MIFF) method, selecting the optimal combinations of the attention-related features for classification, to enhance the attention detection performance. The experimental results showed that the proposed MIFF method outperformed the state-of-the-art methods regardless of data length on both devices. Especially, the proposed method with data length of 2.5 s achieved an average AUC of 0.8505 on the low-cost Sichiray device, which is 56.08 % higher than that of D/B1, 27.28 % higher than that of α + β + δ + θ + R, 17.42 % higher than that of entropy, and 15.48 % higher than that of OCN.