Effect of MEP-oriented scalp acupuncture combined with transcranial magnetic stimulation on limb motor ability in patients with ischemic stroke hemiplegia.

Objectives: To observe the effect of motor evoked potential (MEP)-oriented scalp acupuncture combined with transcranial magnetic stimulation (TMS) on limb motor ability in patients with ischemic stroke hemiplegia.

Methods: A total of 60 patients with ischemic stroke hemiplegia were randomized into an observation group and a control group, 30 cases in each one. In addition to the medication treatment of internal medicine and comprehensive training of hemiplegic limbs, MEP-oriented scalp acupuncture combined with TMS was applied in the observation group, conventional scalp acupuncture at bilateral anterior oblique line of parietal and temporal regions combined with TMS was applied in the control group.

Spatial-temporal network for fine-grained-level emotion EEG recognition.

Electroencephalogram (EEG)-based affective computing brain-computer interfaces provide the capability for machines to understand human intentions. In practice, people are more concerned with the strength of a certain emotional state over a short period of time, which was called as fine-grained-level emotion in this paper. In this study, we built a fine-grained-level emotion EEG dataset that contains two coarse-grained emotions and four corresponding fine-grained-level emotions.

Decoupling representation learning for imbalanced electroencephalography classification in rapid serial visual presentation task.

The class imbalance problem considerably restricts the performance of electroencephalography (EEG) classification in the rapid serial visual presentation (RSVP) task. Existing solutions typically employ re-balancing strategies (e.g.

Phase Preservation Neural Network for Electroencephalography Classification in Rapid Serial Visual Presentation Task.

Neuroscience studies have demonstrated the phase-locked characteristics of some early event-related potential (ERP) components evoked by stimuli. In this study, we propose a phase preservation neural network (PPNN) to learn phase information to improve the Electroencephalography (EEG) classification in a rapid serial visual presentation (RSVP) task. The PPNN consists of three major modules that can produce spatial and temporal representations with the high discriminative ability of the EEG features for classification.

Decoding imagined speech from EEG signals using hybrid-scale spatial-temporal dilated convolution network.

Directly decoding imagined speech from electroencephalogram (EEG) signals has attracted much interest in brain-computer interface applications, because it provides a natural and intuitive communication method for locked-in patients. Several methods have been applied to imagined speech decoding, but how to construct spatial-temporal dependencies and capture long-range contextual cues in EEG signals to better decode imagined speech should be considered.In this study, we propose a novel model called hybrid-scale spatial-temporal dilated convolution network (HS-STDCN) for EEG-based imagined speech recognition.

A Parallel Multiscale Filter Bank Convolutional Neural Networks for Motor Imagery EEG Classification.

Objective: Electroencephalogram (EEG) based brain-computer interfaces (BCI) in motor imagery (MI) have developed rapidly in recent years. A reliable feature extraction method is essential because of a low signal-to-noise ratio (SNR) and time-dependent covariates of EEG signals. Because of efficient application in various fields, deep learning has been adopted in EEG signal processing and has obtained competitive results compared with the traditional methods.