The intelligent recognition of epileptic electroencephalogram (EEG) signals is a valuable tool for the epileptic seizure detection. Recent deep learning models fail to fully consider both spectral and temporal domain representations simultaneously, which may lead to omitting the nonstationary or nonlinear property in epileptic EEGs and further produce a suboptimal recognition performance consequently. In this paper, an end-to-end EEG seizure detection framework is proposed by using a novel channel-embedding spectral-temporal squeeze-andexcitation network (CE-stSENet) with a maximum mean discrepancy-based information maximizing loss. Specifically, the CE-stSENet firstly integrates both multi-level spectral and multiscale temporal analysis simultaneously. Hierarchical multidomain representations are then captured in a unified manner with a variant of squeeze-and-excitation block. The classification net is finally implemented for epileptic EEG recognition based on features extracted in previous subnetworks. Particularly, to address the fact that the scarcity of seizure events results in finite data distribution and the severe overfitting problem in seizure detection, the CE-stSENet is coordinated with a maximum mean discrepancy-based information maximizing loss for mitigating the overfitting problem. Competitive experimental results on three EEG datasets against the state-of-the-art methods demonstrate the effectiveness of the proposed framework in recognizing epileptic EEGs, indicating its powerful capability in the automatic seizure detection.

Motor imagery electroencephalography (EEG) decoding is an essential part in brain-computer interfaces (BCIs) which help motor-disabled patients to communicate with the outside world by external devices. Recently, deep learning algorithms using decomposed spectrums of EEG as inputs may omitted important spatial dependencies and different temporal scale information, thus generated the poor decoding performance. In this paper, we propose an end-to-end EEG decoding framework, which employs raw multi-channel EEG as inputs, to boost decoding accuracy by the channel-projection mixed-scale convolutional neural network (CP-MixedNet) aided by amplitudeperturbation data augmentation. Specifically, the first block in CP-MixedNet is designed to learn primary spatial and temporal representations from EEG signals. The mixed-scale convolutional block is then used to capture mixed-scale temporal information, which effectively reduces the number of training parameters when expanding reception fields of the network. Finally, based on the features extracted in previous blocks, the classification block is constructed to classify EEG tasks. Experiments are implemented on two public EEG datasets (BCI competition IV 2a and High gamma dataset) to validate the effectiveness of the proposed approach comparing to the state-of-the-art methods. The competitive results demonstrate that our proposed method is a promising solution to improve the decoding performance of motor imagery BCIs.

Epileptic seizure detection is commonly implemented by expert clinicians with visual observation of electroencephalography (EEG) signals, which tends to be time consuming and sensitive to bias. The epileptic detection in most previous research suffers from low power and unsuitability for processing large datasets. Therefore, a computerized epileptic seizure detection method is highly required to eradicate the aforementioned problems, expedite epilepsy research and aid medical professionals. In this work, we propose an automatic epilepsy diagnosis framework based on the combination of multi-domain feature extraction and nonlinear analysis of EEG signals. Firstly, EEG signals are pre-processed by using the wavelet threshold method to remove the artifacts. We then extract representative features in the time domain, frequency domain, time-frequency domain and nonlinear analysis features based on the information theory. These features are further extracted in five frequency sub-bands based on the clinical interest, and the dimension of the original feature space is then reduced by using both a principal component analysis and an analysis of variance. Furthermore, the optimal combination of the extracted features is identified and evaluated via different classifiers for the epileptic seizure detection of EEG signals. Finally, the performance of the proposed method is investigated by using a public EEG database at the University Hospital Bonn, Germany. Experimental results demonstrate that the proposed epileptic seizure detection method can achieve a high average accuracy of 99.25%, indicating a powerful method in the detection and classification of epileptic seizures. The proposed seizure detection scheme is thus hoped to eliminate the burden of expert clinicians when they are processing a large number of data by visual observation and to speed-up the epilepsy diagnosis.

Retinal vessel segmentation with deep learning technology is a crucial auxiliary method for clinicians to diagnose fundus diseases. However, the deep learning approaches inevitably lose the edge information, which contains spatial features of vessels while performing down-sampling, leading to the limited segmentation performance of fine blood vessels. Furthermore, the existing methods ignore the dynamic topological correlations among feature maps in the deep learning framework, resulting in the inefficient capture of the channel characterization. To address these limitations, we propose a novel dual encoder-based dynamic-channel graph convolutional network with edge enhancement (DE-DCGCN-EE) for retinal vessel segmentation. Specifically, we first design an edge detection-based dual encoder to preserve the edge of vessels in down-sampling. Secondly, we investigate a dynamic-channel graph convolutional network to map the image channels to the topological space and synthesize the features of each channel on the topological map, which solves the limitation of insufficient channel information utilization. Finally, we study an edge enhancement block, aiming to fuse the edge and spatial features in the dual encoder, which is beneficial to improve the accuracy of fine blood vessel segmentation. Competitive experimental results on five retinal image datasets validate the efficacy of the proposed DE-DCGCN-EE, which achieves more remarkable segmentation results against the other state-of-the-art methods, indicating its potential clinical application.

Electroencephalograph (EEG) emotion recognition plays an important role in the brain-computer interface (BCI) field. However, most of recent methods adopted shallow graph neural networks using a single temporal feature, leading to the limited emotion classification performance. Furthermore, the existing methods generally ignore the individual divergence between different subjects, resulting in poor transfer performance. To address these deficiencies, we propose a dual-branch dynamic graph convolution based adaptive transformer feature fusion network with adapter-finetuned transfer learning (DBGC-ATFFNet-AFTL) for EEG emotion recognition. Specifically, a dual-branch graph convolution network (DBGCN) is firstly designed to effectively capture the temporal and spectral characterizations of EEG simultaneously. Second, the adaptive Transformer feature fusion network (ATFFNet) is conducted by integrating the obtained feature maps with the channel-weight unit, leading to significant difference between different channels. Finally, the adapter-finetuned transfer learning method (AFTL) is applied in cross-subject emotion recognition, which proves to be parameter-efficient with few samples of the target subject. The competitive experimental results on three datasets have shown that our proposed method achieves the promising emotion classification performance compared with the state-of-the-art methods. The code of our proposed method will be available at: https://github.com/smy17/DANet.

Parkinson’s disease (PD) is an incurable neurological disorder that often begins insidiously with sleep disturbances and somatic symptoms, progressing to whole-body motor and cognitive symptoms^1,2,3,4,5. Dysfunction of the somato-cognitive action network (SCAN)—which is thought to control action execution^6,7 by coordinating arousal, organ physiology and whole-body motor plans with behavioural motivation—is a potential contributor to the diverse clinical manifestations of PD. To investigate the role of the SCAN in PD pathophysiology and treatments (medications, deep-brain stimulation (DBS), transcranial magnetic stimulation (TMS) and MRI-guided focused ultrasound stimulation (MRgFUS)), we built a large (n = 863), multimodal, multi-intervention clinical imaging dataset. Resting-state functional connectivity revealed that the substantia nigra and all PD DBS targets (subthalamic nucleus, globus pallidus and ventral intermediate thalamus) are selectively connected to the SCAN rather than to effector-specific motor regions. Importantly, PD was characterized by specific hyperconnectivity between the SCAN and the subcortex. We therefore followed six PD cohorts undergoing DBS, TMS, MRgFUS and levodopa therapy using precision resting-state functional connectivity and electrocorticography recording. Efficacious treatments reduced SCAN-to-subcortex hyperconnectivity. Targeting the SCAN instead of effector regions doubled the efficacy of TMS treatments. Focused ultrasound treatment benefits increased when the target was closer to the thalamic SCAN sweet spot. Thus, SCAN hyperconnectivity is central to PD pathophysiology and its alleviation is a hallmark of successful neuromodulation. Targeting functionally defined subcortical SCAN nodes may improve existing therapies (DBS, MRgFUS), whereas cortical SCAN targets offer effective non-invasive or minimally invasive neuromodulation for PD.