Steady-state visual evoked potentials (SSVEP)-based brain-computer interfaces (BCIs) have the potential to be utilized in various fields due to their high accuracies and information transfer rates (ITR). High-frequency (HF) visual stimuli have shown promise in reducing visual fatigue and enhancing user comfort. However, these HF-SSVEP-BCIs often face limitations in the number of commands and typically require extensive individual training data to achieve high performance. In this study, we proposed a row-column dual-frequency encoding and decoding method using HF stimulation to develop a comfortable BCI system that supports multiple commands and reduces training costs. We arranged 20 targets in a matrix of five rows and four columns, with each target modulated by left-and-right field stimulation using two frequency-phase combinations. Targets in each row or column share a unique frequency-phase combination, allowing EEG data from the same row or column to be used collectively to train a row/column index decoding model for target identification. To evaluate the performance of our method, we constructed a 20-target asynchronous robotic arm control system with the adaptive window method. With only four training trials per target, the online system achieved an ITR of 105.14 ± 14.15 bits/min, a true positive rate of 98.18 ± 2.87%, a false positive rate of 7.39 ± 6.73%, and a classification accuracy of 91.88 ± 5.75%, with an average data length of 925.70 ± 45.44 ms. These results indicate that the proposed protocol can deliver accurate and rapid command outputs for a comfortable SSVEP-based BCI with minimal training data and fewer frequencies.

Transcranial alternating current stimulation (tACS) has been reported to treat refractory auditory hallucinations in schizophrenia. Despite diligent efforts, it is imperative to underscore that tACS does not uniformly demonstrate efficacy across all patients as with all treatments currently employed in clinical practice. The study aims to find biomarkers predicting individual responses to tACS, guiding treatment decisions, and preventing healthcare resource wastage. We divided 17 schizophrenic patients with refractory auditory hallucinations into responsive(RE) and non-responsive(NR) groups based on their auditory hallucination symptom reduction rates after one month of tACS treatment. The pre-treatment resting-state electroencephalogram(rsEEG) was recorded and then computed absolute power spectral density (PSD), Hjorth parameters (HPs, Hjorth activity (HA), Hjorth mobility (HM), and Hjorth complexity (HC) included) from different frequency bands to portray the brain oscillations. The results demonstrated that statistically significant differences localized within the high gamma frequency bands of the right brain hemisphere. Immediately, we input the significant dissociable features into popular machine learning algorithms, the Cascade Forward Neural Network achieved the best recognition accuracy of 93.87%. These findings preliminarily imply that high gamma oscillations in the right brain hemisphere may be the main influencing factor leading to different responses to tACS treatment, and incorporating rsEEG signatures could improve personalized decisions for integrating tACS in clinical treatment.

Body motion is an important channel for human communication and plays a crucial role in automatic emotion recognition. This work proposes a multiscale spatio-temporal network, which captures the coarse-grained and fine-grained affective information conveyed by full-body motion and decodes the complex mapping between emotion and body movement. The proposed method consists of three main components. First, a scale selection algorithm based on the pseudo-energy model is presented, which guides our network to focus not only on long-term macroscopic body expressions, but also on short-term subtle posture changes. Second, we propose a hierarchical spatio-temporal network that can jointly process posture covariance matrices and 3D posture images with different time scales, and then hierarchically fuse them in a coarse-to-fine manner. Finally, a spatio-temporal iterative (ST-ITE) fusion algorithm is developed to jointly optimize the proposed network. The proposed approach is evaluated on five public datasets. The experimental results show that the introduction of the energy-based scale selection algorithm significantly enhances the learning capability of the network. The proposed ST-ITE fusion algorithm improves the generalization and convergence of our model. The average classification results of the proposed method exceed 86% on all datasets and outperform the state-of-the-art methods.

Mental workload (MWL) assessment is critical for accident prevention and operator safety. However, achieving cross-task generalization of MWL classification models is a significant challenge for real-world applications. Classifiers trained on labeled samples from one task often experience a notable performance drop when directly applied to samples from other tasks, limiting its use cases. To address this issue, we propose a semi-supervised cross-task domain adaptation (SCDA) method using power spectral density (PSD) features for MWL recognition across tasks (MATB-II and n-back). Our results demonstrated that the SCDA method achieved the best cross-task classification performance on our data and COG-BCI public dataset, with accuracies of 90.98% ± 9.36% and 96.61% ± 4.35%, respectively. Furthermore, in the cross-task classification of cross-subject scenarios, SCDA showed the highest average accuracy (75.39% ± 9.56% on our data, 90.98% ± 9.36% on the COG-BCI public dataset). The findings indicate that the semi-supervised transfer learning approach using PSD features is feasible and effective for cross-task MWL assessment.

Tens of millions of people suffer from depression worldwide. It is urgent to explore an effective method for diagnosing depression. This study developed a novel of multimodal feature fusion depression recognition method based on functional near-infrared spectroscopy (fNIRS). Sixty volunteers, including thirty patients with depression and thirty healthy controls, participated in the study. The 22-channel fNIRS device recorded the participants' brain oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) concentration changes in the positive, neutral and negative affective words' stimulation. K-nearest neighbors (KNN) and support vector machine (SVM) classifiers were used to recognize depressed patients from normal people, and 10-fold cross-validation was used to verify the classification result. Under the three single-mode features, the accuracy rates were 85.69%, 88.32% and 86.77%, corresponding to the positive condition, neutral condition and negative condition. Then, we used concatenation and linear combination for feature fusion. For the concatenation fusion method, the principal component analysis (PCA) was used to reduce the dimension. The result showed that feature fusion can relatively improve the recognition rate of people with depression, compared with single-model features. The optimal feature fusion method is to concatenate the neutral features and negative features, and the best accuracy reaches 94.45%. The study may provide a more accurate and convenient method for depression detection.

Tens of millions of people suffer from depression worldwide. It is urgent to explore an effective method for diagnosing depression. This study developed a novel of multimodal feature fusion depression recognition method based on functional near-infrared spectroscopy (fNIRS). Sixty volunteers, including thirty patients with depression and thirty healthy controls, participated in the study. The 22-channel fNIRS device recorded the participants’ brain oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) concentration changes in the positive, neutral and negative affective words’ stimulation. K-nearest neighbors (KNN) and support vector machine (SVM) classifiers were used to recognize depressed patients from normal people, and 10-fold cross-validation was used to verify the classification result. Under the three single-mode features, the accuracy rates were 85.69%, 88.32% and 86.77%, corresponding to the positive condition, neutral condition and negative condition. Then, we used concatenation and linear combination for feature fusion. For the concatenation fusion method, the principal component analysis (PCA) was used to reduce the dimension. The result showed that feature fusion can relatively improve the recognition rate of people with depression, compared with single-model features. The optimal feature fusion method is to concatenate the neutral features and negative features, and the best accuracy reaches 94.45%. The study may provide a more accurate and convenient method for depression detection.

As the occurrence of depression in society becomes increasingly more common, it is an urgent task to find more objective and effective tools for real-time depression assessment. Gait analysis offers a new low-cost and contactless method for depression diagnosis. Therefore, interest in gait-based depression detection using depth sensors, such as Kinect, has grown rapidly in recent years. In this paper, a pseudo-velocity model is built to analyze the abnormal gait related to the depression by combining the velocity and angular velocity of the joints. Subsequently, we extract some features in time and frequency domain from our model to establish the classification model for depression detection. Experimental results on depression gait data recordings from 43 scored-depressed and 52 non-depressed individuals show that the proposed method achieves a good classification accuracy of 92.35 % and is superior to other existing methods. The outstanding classification performance suggests that the proposed method has notential clinical value in depression detection.

Emotion, as an advanced function of the human brain, affects kinds of human behaviors. Electroencephalographs (EEG) are widely used in the field of emotion classification owing to their low cost and portability. In this work, we study the effects of a non-linear EEG feature and a channel selection method on emotion recognition. First, the fractal dimension(FD) which could reflect the state of the brain is extracted with a sliding window. The top seven channels are screened out by calculating the F-score from the whole samples. Then, based on the signals from forehead channels, filtered channels and associated channels, emotions on valence and arousal are classified by Support Vector Machine(SVM) and K Nearest Neighbours(KNN). The result shows that the forehead channels Fp2, AF8, Fpz play an important role in valence classification. When combining the forehead channels with other channels that have higher F-score, the SVM classifier has a better accuracy on the whole set with 89.37% on valence and 87.07% on arousal. Besides, the overall accuracy calculated on each participants with associated channels get significant improvement. Especially, the KNN classifier has a much better result on every subject. This phenomenon indicates that by combining the higher F-score channels with the forehead channels, the associated channels can not only take advantage of the forehead channels' ability to categorize emotions but also consider individual differences.