scholarly journals Coherence analysis of EEG in locomotion using graphs

2017 ◽  
Author(s):  
◽  
G. Quiroz
Keyword(s):  
Assistive Devices ◽  
Brain Machine Interface ◽  
Coherence Analysis ◽  
Connectivity Analysis ◽  
Eeg Signals ◽  
Time Frequency ◽  
Temporal Features ◽  
Machine Interface ◽  
Spatio Temporal ◽  
Motion Intention

One of the most interesting brain machine interface (BMI) applications, is the control of assistive devices for rehabilitation of neuromotor pathologies. This means that assistive devices (prostheses, orthoses, or exoskeletons) are able to detect user motion intention, by the acquisition and interpretation of electroencephalographic (EEG) signals. Such interpretation is based on the time, frequency or space features of the EEG signals. For this reason, in this paper a coherence-based EEG study is proposed during locomotion that along with the graph theory allows to establish spatio-temporal parameters that are characteristic in this study. The results show that along with the temporal features of the signal it is possible to find spatial patterns in order to classify motion tasks of interest. In this manner, the connectivity analysis alongside graphs provides reliable information about the spatio-temporal characteristics of the neural activity, showing a dynamic pattern in the connectivity during locomotions tasks.

scholarly journals Coherence analysis of EEG in locomotion using graphs

2017 ◽  
Author(s):  
◽  
G. Quiroz
Keyword(s):  
Assistive Devices ◽  
Brain Machine Interface ◽  
Coherence Analysis ◽  
Connectivity Analysis ◽  
Eeg Signals ◽  
Time Frequency ◽  
Temporal Features ◽  
Machine Interface ◽  
Spatio Temporal ◽  
Motion Intention

One of the most interesting brain machine interface (BMI) applications, is the control of assistive devices for rehabilitation of neuromotor pathologies. This means that assistive devices (prostheses, orthoses, or exoskeletons) are able to detect user motion intention, by the acquisition and interpretation of electroencephalographic (EEG) signals. Such interpretation is based on the time, frequency or space features of the EEG signals. For this reason, in this paper a coherence-based EEG study is proposed during locomotion that along with the graph theory allows to establish spatio-temporal parameters that are characteristic in this study. The results show that along with the temporal features of the signal it is possible to find spatial patterns in order to classify motion tasks of interest. In this manner, the connectivity analysis alongside graphs provides reliable information about the spatio-temporal characteristics of the neural activity, showing a dynamic pattern in the connectivity during locomotions tasks.

scholarly journals National Institute of Neurological Disorders and Stroke support for brain-machine interface technology

2009 ◽  
Vol 27 (1) ◽  
pp. E14 ◽  
Author(s):  
Joseph J. Pancrazio
Keyword(s):  
Neurological Disorders ◽  
Assistive Devices ◽  
National Institutes Of Health ◽  
Brain Machine Interface ◽  
Multidisciplinary Teams ◽  
Neural Engineering ◽  
Design Development ◽  
Volitional Control ◽  
History Of ◽  
Machine Interface

Brain-machine interfaces (BMIs) offer the promise of restoring communication, enabling control of assistive devices, and allowing volitional control of extremities in paralyzed individuals. Working in multidisciplinary teams, neurosurgeons can play an invaluable role in the design, development, and demonstration of novel BMI technology. At the National Institutes of Health, the National Institute of Neurological Disorders and Stroke has a long history of supporting neural engineering and prosthetics efforts including BMI, and these research opportunities continue today. The author provides a brief overview of the opportunities and programs currently available to support BMI projects.

scholarly journals EEG-Based 3D Visual Fatigue Evaluation Using CNN

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1208 ◽  
Author(s):  
Kang Yue ◽  
Danli Wang
Keyword(s):  
Feature Extraction ◽  
Temporal Structure ◽  
Visual Fatigue ◽  
Eeg Signals ◽  
Visual Evaluation ◽  
Time Frequency ◽  
Multi Scale ◽  
Temporal Features ◽  
Depth Analysis ◽  
Fatigue Evaluation

Visual fatigue evaluation plays an important role in applications such as virtual reality since the visual fatigue symptoms always affect the user experience seriously. Existing visual evaluation methods require hand-crafted features for classification, and conduct feature extraction and classification in a separated manner. In this paper, we conduct a designed experiment to collect electroencephalogram (EEG) signals of various visual fatigue levels, and present a multi-scale convolutional neural network (CNN) architecture named MorletInceptionNet to detect visual fatigue using EEG as input, which exploits the spatial-temporal structure of multichannel EEG signals. Our MorletInceptionNet adopts a joint space-time-frequency features extraction scheme in which Morlet wavelet-like kernels are used for time-frequency raw feature extraction and inception architecture are further used to extract multi-scale temporal features. Then, the multi-scale temporal features are concatenated and fed to the fully connected layer for visual fatigue evaluation using classification. In experiment evaluation, we compare our method with five state-of-the-art methods, and the results demonstrate that our model achieve overally the best performance better performance for two widely used evaluation metrics, i.e., classification accuracy and kappa value. Furthermore, we use input-perturbation network-prediction correlation maps to conduct in-depth analysis into the reason why the proposed method outperforms other methods. The results suggest that our model is sensitive to the perturbation of β (14–30 Hz) and γ (30–40 Hz) bands. Furthermore, their spatial patterns are of high correlation with that of the corresponding power spectral densities which are used as evaluation features traditionally. This finding provides evidence of the hypothesis that the proposed model can learn the joint time-frequency-space features to distinguish fatigue levels automatically.

scholarly journals Security Solutions Using Brain Signals

2018 ◽  
Vol 7 (2) ◽  
pp. 105
Author(s):  
Anupama. H.S ◽  
Anusha M ◽  
Aparna Joshi ◽  
Apoorva N ◽  
N.K. Cauvery ◽  
...  
Keyword(s):  
Current Model ◽  
Neural Interface ◽  
Brain Machine Interface ◽  
Eeg Signals ◽  
Brain Signals ◽  
Thought Pattern ◽  
Machine Interface ◽  
Image Signature ◽  
The Brain ◽  
Communication Path

A Brain Computer Interface is a direct neural interface or a brain–machine interface. It provides a communication path between human brain and the computer system. It aims to convey people's intentions to the outside world directly from their thoughts. This paper focuses on current model which uses brain signals for the authentication of users. The Electro- Encephalogram (EEG) signals are recorded from the neuroheadset when a user is shown a key image (signature image). These signals are further processed and are interpreted to obtain the thought pattern of the user to match them to the stored password in the system. Even if other person is presented with the same key image it fails to authenticate as the cortical folds of the brain are unique to each human being just like a fingerprint or DNA.

Clinical Diagnosis of Psychiatry Based on Electroencephalography

2021 ◽  
Vol 11 (3) ◽  
pp. 955-963
Author(s):  
Lixue Yuan ◽  
Yinyan Fan ◽  
Quanxi Gan ◽  
Huibin Feng
Keyword(s):  
Environmental Control ◽  
Low Cost ◽  
Training System ◽  
Brain Machine Interface ◽  
Signal Acquisition ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Feedback Information ◽  
Machine Interface ◽  
Neural Feedback

At present, neurophysiological signals used for neuro feedback are EEG (Electroencephalogram), functional magnetic resonance imaging. Among them, the acquisition of EEG signals has the advantages of non-invasive way with low cost. It has been widely used in brain-machine interface technology in recent years. Important progress has been made in rehabilitation and environmental control. However, neural feedback and brainmachine interface technology are completely similar in signal acquisition, signal feature extraction, and pattern classification. Therefore, the related research results of brain-machine interface can be used to closely cooperate with clinical needs to research and develop neural feedback technology based on EEG. Based on neurophysiology and brain-machine interface technology, this paper develops a neural feedback training system based on the acquisition and analysis of human EEG signals. Aiming at the autonomous rhythm components in the EEG signal, such as sensorimotor rhythm and alpha rhythm, the characteristic parameters are extracted through real-time EEG signal processing to generate feedback information, and the subject is self-regulated and trained from a physiological-psychological perspective by providing adjuvant treatment, a practical and stable treatment platform for the clinic.

scholarly journals Classification of Relaxation and Concentration Mental States with EEG

Information ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 187
Author(s):  
Shingchern D. You
Keyword(s):  
Mental States ◽  
Support Vector ◽  
Svm Classifier ◽  
Eeg Signals ◽  
Consumer Market ◽  
Time Frequency ◽  
Average Accuracy ◽  
Spectral Components ◽  
Machine Interface

In this paper, we study the use of EEG (Electroencephalography) to classify between concentrated and relaxed mental states. In the literature, most EEG recording systems are expensive, medical-graded devices. The expensive devices limit the availability in a consumer market. The EEG signals are obtained from a toy-grade EEG device with one channel of output data. The experiments are conducted in two runs, with 7 and 10 subjects, respectively. Each subject is asked to silently recite a five-digit number backwards given by the tester. The recorded EEG signals are converted to time-frequency representations by the software accompanying the device. A simple average is used to aggregate multiple spectral components into EEG bands, such as α, β, and γ bands. The chosen classifiers are SVM (support vector machine) and multi-layer feedforward network trained individually for each subject. Experimental results show that features, with α+β+γ bands and bandwidth 4 Hz, the average accuracy over all subjects in both runs can reach more than 80% and some subjects up to 90+% with the SVM classifier. The results suggest that a brain machine interface could be implemented based on the mental states of the user even with the use of a cheap EEG device.

scholarly journals Learning Regional Attention Convolutional Neural Network for Motion Intention Recognition Based on EEG Data

2020 ◽  
Author(s):  
Zhijie Fang ◽  
Weiqun Wang ◽  
Shixin Ren ◽  
Jiaxing Wang ◽  
Weiguo Shi ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Decoding Algorithms ◽  
Time Frequency ◽  
Intention Recognition ◽  
Temporal Features ◽  
Eeg Data ◽  
Benchmark Datasets ◽  
Critical Regions ◽  
Motion Intention

Recent deep learning-based Brain-Computer Interface (BCI) decoding algorithms mainly focus on spatial-temporal features, while failing to explicitly explore spectral information which is one of the most important cues for BCI. In this paper, we propose a novel regional attention convolutional neural network (RACNN) to take full advantage of spectral-spatial-temporal features for EEG motion intention recognition. Time-frequency based analysis is adopted to reveal spectral-temporal features in terms of neural oscillations of primary sensorimotor. The basic idea of RACNN is to identify the activated area of the primary sensorimotor adaptively. The RACNN aggregates a varied number of spectral-temporal features produced by a backbone convolutional neural network into a compact fixed-length representation. Inspired by the neuroscience findings that functional asymmetry of the cerebral hemisphere, we propose a region biased loss to encourage high attention weights for the most critical regions. Extensive evaluations on two benchmark datasets and real-world BCI dataset show that our approach significantly outperforms previous methods.

Gesture Recognition Via Learning Deep Spatio-Temporal Features In Wi-Fi Sensing

2021 ◽  
Author(s):  
Jianxiao Xie ◽  
Wei Ye ◽  
Kai Xu
Keyword(s):  
Gesture Recognition ◽  
Efficient Manner ◽  
Time Frequency ◽  
3D Data ◽  
Temporal Features ◽  
Data Segment ◽  
Spatio Temporal ◽  
3D Cnn ◽  
Free Gesture ◽  
Device Free

Abstract Internet of Things (IoT) expects to incorporate massive machine-type (MCT) devices, such as vehicles, sensors, and wearable devices, which brings a large number of application tasks that need to be processed. Additionally, data collected from various devices needs to be executed and processed in a timely, reliable, and efficient manner. Gesture recognition has enabled IoT applications such as human-computer interaction and virtual reality. In this work, we propose a cross-domain device-free gesture recognition (DFGR) model, that exploits 3D-CNN to obtain spatiotemporal features in Wi-Fi sensing. To adapt the sensing data to the 3D model, we carry out 3D data segment and supplement in addition to signal denoising and time-frequency transformation. We demonstrate that our proposed model outperforms the state-of-the-art method in the application of DFGR even cross 3 domain factors simultaneously, and is easy to converge and convenient for training with a less complicated hierarchical structure.

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