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Rapid Serial Visual Presentation (RSVP)-based Brain-Computer Interface (BCI) is an efficient information detection technology by detecting event-related brain responses evoked by target visual stimuli. However, a time-consuming calibration procedure is needed before a new user can use this system. Thus, it is important to reduce calibration efforts for BCI applications. In this article, we propose a multi-source conditional adversarial domain adaptation with the correlation metric learning (mCADA-C) framework that utilizes data from other subjects to reduce the data requirement from the new subject for training the model. This model utilizes adversarial training to enable a CNN-based feature extraction network to extract common features from different domains. A correlation metric learning (CML) loss is proposed to constrain the correlation of features based on class and domain to maximize the intra-class similarity and minimize inter-class similarity. Also, a multi-source framework with a source selection strategy is adopted to integrate the results of multiple domain adaptation. We constructed an RSVP-based dataset that includes 11 subjects each performing three RSVP experiments on three different days. The experimental results demonstrate that our proposed method can achieve 87.72% cross-subject balanced-accuracy under one block calibration. The results indicate our method can realize a higher performance with less calibration efforts.

Motor imagery (MI) is an important brain-computer interface (BCI) paradigm, which can be applied without external stimulus. Imagining different joint movements from the same limb allows intuitive control of the outer devices. However, few researches focused on this field, and the decoding accuracy limited the applications for practical use. In this study, we aim to use deep learning methods to explore the ceiling of the decoding performance of three tasks: the resting state, the MI of right hand and right elbow. To represent the brain functional relationships, the correlation matrix that consists of correlation coefficients between electrodes (channels) was calculated as features. We proposed the Channel-Correlation Network to learn the overall representation among channels for classification. Ensemble learning was applied to integrate the output of multiple Channel-Correlation Networks. Our proposed method achieved the decoding accuracy of up to 87.03% in the 3-class scenario. The results demonstrated the effectiveness of deep learning method for decoding MI of different joints from the same limb and the potential of this fine paradigm to be applied in practice.

Background The aim of this cross-sectional study to assess the proportions of anxiety and depression in patients with CuTS, and to explore the associated demographic and clinical features. Methods From May 2011 to January 2017, 246 patients diagnosed with CuTS were recruited. The Hospital Anxiety and Depression Scale was used to assess the proportions of depression and anxiety. Patient demographic and clinical data were collected. Univariate analysis and multivariate regression were carried out to identify the variables that were independently associated with anxiety and depression. Results The proportions of depression and anxiety were 17.9% (n = 44) and 14.2% (n = 35), respectively. Five patients had both possible/probable anxiety and depression. Logistic regression analysis revealed that diabetes mellitus was independently associated with depression; and the modified McGowan grade was independently associated with anxiety. Conclusions In patients with CuTS, the proportions of depression and anxiety were 17.9% and 14.2%, respectively. Early screening for anxiety and depression is beneficial for patients with CuTS.

Rapid Serial Visual Presentation (RSVP)-based Brain-Computer Interface (BCI) is an efficient information detection technology by detecting event-related brain responses evoked by target visual stimuli. However, a time-consuming calibration procedure is needed before a new user can use this system. Thus, it is important to reduce calibration efforts for BCI applications. In this paper, we collect an RSVP-based electroencephalogram (EEG) dataset, which includes 11 subjects. The experimental task is image retrieval. Also, we propose a multi-source transfer learning framework by utilizing data from other subjects to reduce the data requirement on the new subject for training the model. A source-selection strategy is firstly adopted to avoid negative transfer. And then, we propose a transfer learning network based on domain adversarial training. The convolutional neural network (CNN)-based network is designed to extract common features of EEG data from different subjects, while the discriminator tries to distinguish features from different subjects. In addition, a classifier is added for learning semantic information. Also, conditional information and gradient penalty are added to enable stable training of the adversarial network and improve performance. The experimental results demonstrate that our proposed method outperforms a series of state-of-the-art and baseline approaches.

Motor imagery (MI) is one of the important brain-computer interface (BCI) paradigms, which can be used to control peripherals without external stimulus. Imagining the movements of different joints of the same limb allows intuitive control of the outer devices. In this report, we describe an open access multi-subject dataset for MI of different joints from the same limb. This experiment collected data from twenty-five healthy subjects on three tasks: 1) imagining the movement of right hand, 2) imagining the movement of right elbow, and 3) keeping resting with eyes open, which results in a total of 22,500 trials. The dataset provided includes data of three stages: 1) raw recorded data, 2) pre-processed data after operations such as artifact removal, and 3) trial data that can be directly used for feature extraction and classification. Different researchers can reuse the dataset according to their needs. We expect that this dataset will facilitate the analysis of brain activation patterns of the same limb and the study of decoding techniques for MI., Measurement(s)brain measurement • functional brain measurementTechnology Type(s)electroencephalography (EEG) • silver/silver chloride reference electrodeFactor Type(s)type of motion imagery taskSample Characteristic - OrganismHomo sapiens Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12278444

The acute primary (first-time) lateral patellar dislocation is associated with a high rate of functional disability. There is no consensus as to the choice of surgical or non-surgical treatment for these patients. The aim of this study is to compare the clinical results between the surgical (reconstruction of the medial patellofemoral ligament [MPFL]) and non-surgical treatments for acute primary patellar dislocations. Sixty-nine skeletally mature patients (69 knees) were included in this prospective non-randomized controlled trial. At least one predisposing factor for patellar dislocation (including patella alta, high lateral patellar tilt, trochlear dysplasia, and increased TT-TG distance) was identified in the included patients. Thirty patients were treated surgically with MPFL reconstruction, and the other 39 patients were treated non-surgically. The main outcome variable was patellar redislocation within a two year follow-up period. The Kujala questionnaire was applied for analyzing the pain and the quality of life. The additional surgeries due to patellofemoral problems were also recorded. Patellar redislocation occurred in eight patients in the non-surgical group, while no redislocation occurred in the surgical group (P

Emotion recognition is an important field of research in Affective Computing (AC), and the EEG signal is one of useful signals in detecting and evaluating emotion. With the development of the deep learning, the neural network is widely used in constructing the EEG-based emotion recognition model. In this paper, we propose an effective similarity learning network, on the basis of a bidirectional long short term memory (BLSTM) network. The pairwise constrain loss will help to learn a more discriminative embedding feature space, combined with the traditional supervised classification loss function. The experiment result demonstrates that the pairwise constrain loss can significantly improve the emotion classification performance. In addition, our method outperforms the state-of-the- art emotion classification approaches in the benchmark EEG emotion dataset-SEED dataset, which get a mean accuracy of 94.62%.

Emotions play an important role in human communication, and EEG signals are widely used for emotion recognition. Despite the extensive research of EEG in recent year, it is still challenging to interpret EEG signals effectively due to the massive noises in EEG signals. In this paper, we propose an effective emotion recognition framework, which contains two main parts: the representation network and the prototype selection algorithm. Through our proposed representation network, samples from the same kind of emotion state are more close to each other in high-level representation, and then, we selected the prototypes from the clustering set in feature space match the following testing samples. This method takes advantage of the powerful representation ability of deep learning and learns a better describable feature space rather than learn the classifier explicitly. The experiments on SEED dataset achieves a high accuracy of 93.29% and outperforms a set of baseline methods and the recent deep learning emotion classification approaches. These experimental results demonstrate the effectiveness of our proposed emotion recognition framework.

Motor imagery (MI) based Brain-Computer Interface (BCI) is an important active BCI paradigm for recognizing movement intention of severely disabled persons. There are extensive studies about MI-based intention recognition, most of which heavily rely on staged handcrafted EEG feature extraction and classifier design. For end-to-end deep learning methods, researchers encode spatial information with convolution neural networks (CNNs) from raw EEG data. Compared with CNNs, recurrent neural networks (RNNs) allow for long-range lateral interactions between features. In this paper, we proposed a pure RNNs-based parallel method for encoding spatial and temporal sequential raw data with bidirectional Long Short- Term Memory (bi-LSTM) and standard LSTM, respectively. Firstly, we rearranged the index of EEG electrodes considering their spatial location relationship. Secondly, we applied sliding window method over raw EEG data to obtain more samples and split them into training and testing sets according to their original trial index. Thirdly, we utilized the samples and their transposed matrix as input to the proposed pure RNNs- based parallel method, which encodes spatial and temporal information simultaneously. Finally, the proposed method was evaluated in the public MI-based eegmmidb dataset and compared with the other three methods (CSP+LDA, FBCSP+LDA, and CNN-RNN method). The experiment results demonstrated the superior performance of our proposed pure RNNs-based parallel method. In the multi-class trial-wise movement intention classification scenario, our approach obtained an average accuracy of 68.20% and significantly outperformed other three methods with an 8.25% improvement of relative accuracy on average, which proves the feasibility of our approach for the real-world BCI system.

Epilepsy afflicts nearly 1% of the world’s population, and is characterized by the occurrence of spontaneous seizures. It’s important to make prediction before seizures, so that epileptic can prevent seizures taking place on some specific occasions to avoid suffering from great damage. The previous work in seizure prediction paid less attention to the time-series information and their performances may also restricted to the small training data. In this study, we proposed a Long Short-Term Memory (LSTM)-based multi-task learning (MTL) framework for seizure prediction. The LSTM unit was used to process the sequential data and the MTL framework was applied to perform prediction and latency regression simultaneously. We evaluated the proposed method in the American Epilepsy Society Seizure Prediction Challenge dataset and obtained an average prediction accuracy of 89.36%, which was 3.41% higher than the reported state-of-the-art. In addition, the input data and output of middle layers were visualized. The visual and experiment results demonstrated the superior performance of our proposed LSTM-MTL method for seizure prediction.