Category: Journal of Neural Engineering

Objective . Attention-deficit/hyperactivity disorder (ADHD) is one of the most prevalent neurobehavioral disorders. Studies have tried to find the neural correlations of ADHD with electroencephalography (EEG). Due to the heterogeneity in the ADHD population, a multivariate EEG profile is useful, and the detection of a personalized abnormality in EEG is urgently needed. Deep learning algorithms, especially convolutional neural network (CNN), have made tremendous progress recently, and are expected to solve the problem. Approach . We adopted CNN techniques and a visualization technique named gradient-weighted class activation mapping (Grad-CAM) for detecting a personalized spatial-frequency abnormality in EEGs of ADHD children. A total of 50 children with ADHD (nine girls, mean age: 10.44  ±  0.75) and 57 controls who were matched for age and handedness were recruited. The power spectrum density of EEGs was used as input. We presented an intuitive form of representing...

Objective . The auditory attention decoding (AAD) approach can be used to determine the identity of the attended speaker during an auditory selective attention task, by analyzing measurements of electroencephalography (EEG) data. The AAD approach has the potential to guide the design of speech enhancement algorithms in hearing aids, i.e. to identify the speech stream of listener’s interest so that hearing aids algorithms can amplify the target speech and attenuate other distracting sounds. This would consequently result in improved speech understanding and communication and reduced cognitive load, etc. The present work aimed to investigate whether additional visual input (i.e. lipreading) would enhance the AAD performance for normal-hearing listeners. Approach . In a two-talker scenario, where auditory stimuli of audiobooks narrated by two speakers were presented, multi-channel EEG signals were recorded while participants were selectively attending to one speaker an...

Objective . Information encoding in neurons can be described through their response fields. The spatial response field of a neuron is the region of space in which a sensory stimulus or a behavioral event causes that neuron to fire. Neurons can also exhibit temporal response fields (TRFs), which characterize a transient response to stimulus or behavioral event onsets. These neurons can thus be described by a spatio-temporal response field (STRF). The activity of neurons with STRFs can be well-described with point process models that characterize binary spike trains with an instantaneous firing rate that is a function of both time and space. However, developing decoders for point process models of neurons that exhibit TRFs is challenging because it requires prior knowledge of event onset times, which are unknown. Indeed, point process filters (PPF) to date have largely focused on decoding neuronal activity without considering TRFs. Also, neural classifiers have required dat...

Objective . Learning the structures and unknown correlations of a motor imagery electroencephalogram (MI-EEG) signal is important for its classification. It is also a major challenge to obtain good classification accuracy from the increased number of classes and increased variability from different people. In this study, a four-class MI task is investigated. Approach . An end-to-end novel hybrid deep learning scheme is developed to decode the MI task from EEG data. The proposed algorithm consists of two parts: a . A one-versus-rest filter bank common spatial pattern is adopted to preprocess and pre-extract the features of the four-class MI signal. b . A hybrid deep network based on the convolutional neural network and long-term short-term memory network is proposed to extract and learn the spatial and temporal features of the MI signal simultaneously. Main results . The main contribution of this paper is to propose a hybrid deep network framework t...

Objective . Close-loop control of brain and behavior will benefit from real-time detection of behavioral events to enable low-latency communication with peripheral devices. In animal experiments, this is typically achieved by using sparsely distributed (embedded) sensors that detect animal presence in select regions of interest. High-speed cameras provide high-density sampling across large arenas, capturing the richness of animal behavior, however, the image processing bottleneck prohibits real-time feedback in the context of rapidly evolving behaviors. Approach . Here we developed an open-source software, named PolyTouch, to track animal behavior in large arenas and provide rapid close-loop feedback in ~5.7 ms, ie. average latency from the detection of an event to analog stimulus delivery, e.g. auditory tone, TTL pulse, when tracking a single body. This stand-alone software is written in JAVA. The included wrapper for MATLAB provides experimental flexibility for da...

Objective . The goal of this study was to perform real-time estimation of isometric finger extension force using the discharge information of motor units (MUs). Approach . A real-time electromyogram (EMG) decomposition method based on the fast independent component analysis (FastICA) algorithm was developed to extract MU discharge events from high-density (HD) EMG recordings. The decomposition was first performed offline during an initialization period, and the obtained separation matrix was then applied to new data samples in real-time. Since MU pool discharge probability reflects the neural drive to spinal motoneurons, individual finger forces were estimated based on a firing rate-force model established during the initialization, termed the neural-drive method. The conventional EMG amplitude-based method was used to estimate the forces as a comparison, termed the EMG-amplitude method. Simulated HD-EMG signals were first used to evaluate the accuracy of the real-t...

Objective . Since noise is inevitably introduced during the measurement process of surface electromyographic (sEMG) signals, two novel methods for denoising based on the variational mode decomposition (VMD) method were proposed in this work. Prior to this study, there has been no literature relating to how VMD is applied to sEMG denoising. Approach . The first proposed method uses the VMD method to decompose the signal into multiple variational mode functions (VMFs), each of which has its own center frequency and narrow band, and then the wavelet soft thresholding (WST) method is applied to each VMF. This method is termed the VMD-WST. The second proposed method uses the VMD method to decompose the signal into multiple VMFs, and then the soft interval thresholding (SIT) method is performed on each VMF, which is abbreviated as VMD-SIT. Ten healthy subjects and ten stroke patients participated in the experiment, and the sEMG signals of bicep brachii were measured and a...