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BackgroundThe fatigue that users suffer when using steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) can cause a number of serious problems such as signal quality degradation and system performance deterioration, users’ discomfort and even risk of photosensitive epileptic seizures, posing heavy restrictions on the applications of SSVEP-based BCIs. Towards alleviating the fatigue, a fundamental step is to measure and evaluate it but most existing works adopt self-reported questionnaire methods which are subjective, offline and memory dependent. This paper proposes an objective and real-time approach based on electroencephalography (EEG) spectral analysis to evaluate the fatigue in SSVEP-based BCIs.MethodsHow the EEG indices (amplitudes in δ, θ, α and β frequency bands), the selected ratio indices (θ/α and (θ + α)/β), and SSVEP properties (amplitude and signal-to-noise ratio (SNR)) changes with the increasing fatigue level are investigated through two elaborate SSVEP-based BCI experiments, one validates mainly the effectiveness and another considers more practical situations. Meanwhile, a self-reported fatigue questionnaire is used to provide a subjective reference. ANOVA is employed to test the significance of the difference between the alert state and the fatigue state for each index.ResultsConsistent results are obtained in two experiments: the significant increases in α and (θ + α)/β, as well as the decrease in θ/α are found associated with the increasing fatigue level, indicating that EEG spectral analysis can provide robust objective evaluation of the fatigue in SSVEP-based BCIs. Moreover, the results show that the amplitude and SNR of the elicited SSVEP are significantly affected by users’ fatigue.ConclusionsThe experiment results demonstrate the feasibility and effectiveness of the proposed method as an objective and real-time evaluation of the fatigue in SSVEP-based BCIs. This method would be helpful in understanding the fatigue problem and optimizing the system design to alleviate the fatigue in SSVEP-based BCIs.

Objective. Latest target recognition methods that are equipped with learning from the subject’s calibration data, represented by the extended canonical correlation analysis (eCCA) and the ensemble task-related component analysis (eTRCA), can achieve extra high performance in the steady-state visual evoked potential (SSVEP)-based brain–computer interfaces (BCIs), however their performance deteriorate drastically if the calibration trials are insufficient. This paper develops a new scheme to learn from limited calibration data. Approach. A learning across multiple stimuli scheme is proposed for the target recognition methods, which applies to learning the data corresponding to not only the target stimulus but also the other stimuli. The resulting optimization problems can be simplified and solved utilizing the prior knowledge and properties of SSVEPs across different stimuli. With the new learning scheme, the eCCA and the eTRCA can be extended to the multi-stimulus eCCA (ms-eCCA) and the multi-stimulus eTRCA (ms-eTRCA), respectively, as well as a combination of them (i.e. ms-eCCA+ms-eTRCA) that incorporates their merits. Main results. Evaluation and comparison using an SSVEP-BCI benchmark dataset with 35 subjects show that the ms-eCCA (or ms-eTRCA) performs significantly better than the eCCA (or eTRCA) method while the ms-eCCA+ms-eTRCA performs the best. With the learning across stimuli scheme, the existing target recognition methods can be further improved in terms of the target recognition performance and the ability against insufficient calibration. Significance. A new learning scheme is proposed towards the efficient use of the calibration data, providing enhanced performance and saving calibration time in the SSVEP-based BCIs.

Recently, multilayer extreme learning machine (ML-ELM) was applied to stacked autoencoder (SAE) for representation learning. In contrast to traditional SAE, the training time of ML-ELM is significantly reduced from hours to seconds with high accuracy. However, ML-ELM suffers from several drawbacks: 1) manual tuning on the number of hidden nodes in every layer is an uncertain factor to training time and generalization; 2) random projection of input weights and bias in every layer of ML-ELM leads to suboptimal model generalization; 3) the pseudoinverse solution for output weights in every layer incurs relatively large reconstruction error; and 4) the storage and execution time for transformation matrices in representation learning are proportional to the number of hidden layers. Inspired by kernel learning, a kernel version of ML-ELM is developed, namely, multilayer kernel ELM (ML-KELM), whose contributions are: 1) elimination of manual tuning on the number of hidden nodes in every layer; 2) no random projection mechanism so as to obtain optimal model generalization; 3) exact inverse solution for output weights is guaranteed under invertible kernel matrix, resulting to smaller reconstruction error; and 4) all transformation matrices are unified into two matrices only, so that storage can be reduced and may shorten model execution time. Benchmark data sets of different sizes have been employed for the evaluation of ML-KELM. Experimental results have verified the contributions of the proposed ML-KELM. The improvement in accuracy over benchmark data sets is up to 7%.

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Neurofeedback training, which enables the trainee to learn self-control of the EEG activity of interest based on online feedback, has demonstrated benefits on cognitive and behavioral performance. Nevertheless, as a core mechanism of neurofeedback, learning of EEG regulation (i.e., EEG learning) has not been well understood. Moreover, a substantial number of non-learners who fail to achieve successful EEG learning have often been reported. This study investigated the EEG learning in alpha down-regulation neurofeedback, aiming to better understand the alpha learning and to early predict learner/non-learner. Twenty-nine participants received neurofeedback training to down-regulate alpha in two days, while eight of them were identified as non-learners who failed to reduce their alpha within sessions. Through a stepwise linear discriminant analysis, a prediction model was built based on participant’s eyes-closed resting EEG activities in broad frequency bands including lower alpha, theta, sigma and beta 1 measured before training, which was validated in predicting learners/non-learners. The findings would assist in the early identification of the individuals who would not likely reduce their alpha during neurofeedback.