scholarly journals Detection of Fast and Slow Hand Movements from Motor Imagery EEG Signals

2014 ◽  
pp. 645-652 ◽  
Author(s):  
Saugat Bhattacharyya ◽  
Munshi Asif Hossain ◽  
Amit Konar ◽  
D. N. Tibarewala ◽  
Janarthanan Ramadoss
Keyword(s):  
Motor Imagery ◽  
Hand Movements ◽  
Eeg Signals

scholarly journals Validating Deep Neural Networks for Online Decoding of Motor Imagery Movements from EEG Signals

Sensors ◽  
2019 ◽  
Vol 19 (1) ◽  
pp. 210 ◽  
Author(s):  
Zied Tayeb ◽  
Juri Fedjaev ◽  
Nejla Ghaboosi ◽  
Christoph Richter ◽  
Lukas Everding ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Motor Imagery ◽  
Classification Performance ◽  
Feature Engineering ◽  
Learning Models ◽  
Eeg Signals ◽  
Learning Methods

Non-invasive, electroencephalography (EEG)-based brain-computer interfaces (BCIs) on motor imagery movements translate the subject’s motor intention into control signals through classifying the EEG patterns caused by different imagination tasks, e.g., hand movements. This type of BCI has been widely studied and used as an alternative mode of communication and environmental control for disabled patients, such as those suffering from a brainstem stroke or a spinal cord injury (SCI). Notwithstanding the success of traditional machine learning methods in classifying EEG signals, these methods still rely on hand-crafted features. The extraction of such features is a difficult task due to the high non-stationarity of EEG signals, which is a major cause by the stagnating progress in classification performance. Remarkable advances in deep learning methods allow end-to-end learning without any feature engineering, which could benefit BCI motor imagery applications. We developed three deep learning models: (1) A long short-term memory (LSTM); (2) a spectrogram-based convolutional neural network model (CNN); and (3) a recurrent convolutional neural network (RCNN), for decoding motor imagery movements directly from raw EEG signals without (any manual) feature engineering. Results were evaluated on our own publicly available, EEG data collected from 20 subjects and on an existing dataset known as 2b EEG dataset from “BCI Competition IV”. Overall, better classification performance was achieved with deep learning models compared to state-of-the art machine learning techniques, which could chart a route ahead for developing new robust techniques for EEG signal decoding. We underpin this point by demonstrating the successful real-time control of a robotic arm using our CNN based BCI.

Pattern Recognition of EEG Signals During Motor Imagery

2006 ◽  
Author(s):  
Koichi Nagata ◽  
Makoto Mihara ◽  
Tomonari Yamagutchi ◽  
Miyo Taniguchi ◽  
Katsuhiro Inoue ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Motor Imagery ◽  
Eeg Signals

Pattern recognition of EEG signals during right and left motor imagery

2003 ◽  
Vol 36 (16) ◽  
pp. 139-144 ◽  
Author(s):  
Katsuhiro Inoue ◽  
Gert Pfurtscheller ◽  
Christa Neuper ◽  
Kousuke Kumamaru
Keyword(s):  
Pattern Recognition ◽  
Motor Imagery ◽  
Eeg Signals

Robust classification of motor imagery EEG signals using statistical time–domain features

2013 ◽  
Vol 34 (11) ◽  
pp. 1563-1579 ◽  
Author(s):  
A Khorshidtalab ◽  
M J E Salami ◽  
M Hamedi
Keyword(s):  
Motor Imagery ◽  
Time Domain ◽  
Eeg Signals ◽  
Robust Classification ◽  
Statistical Time

Identification of motor imagery tasks using power-based connectivity descriptors from EEG signals

2021 ◽  
Author(s):  
Cristian David Guerrero-Mendez ◽  
Cristian Felipe Blanco-Diaz ◽  
Andres Felipe Ruiz-Olaya
Keyword(s):  
Motor Imagery ◽  
Eeg Signals

Classification of EEG Signals for Motor Imagery Based on Mutual Information and Adaptive Neuro Fuzzy Inference System

Fuzzy Systems ◽  
2017 ◽  
pp. 347-366
Author(s):  
Shereen A. El-aal ◽  
Rabie A. Ramadan ◽  
Neveen Ghali
Keyword(s):  
Mutual Information ◽  
Motor Imagery ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Imagery Task ◽  
Eeg Signals ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Anfis Classifier ◽  
Maximal Relevance

Electroencephalogram (EEG) signals based Brain Computer Interface (BCI) is employed to help disabled people to interact better with the environment. EEG signals are recorded through BCI system to translate it to control commands. There are a large body of literature targeting EEG feature extraction and classification for Motor Imagery tasks. Motor imagery task have several features can be extracted to use in classification. However, using more features consume running time and using irrelevant and redundant features affect the performance of the used classifier. This paper is dedicated to extracting the best feature vector for motor imagery task. This work suggests two feature selection methods based on Mutual Information (MI) including Minimum Redundancy Maximal Relevance (MRMR) and maximal Relevance (MaxRel). Adaptive Neuro Fuzzy Inference System (ANFIS) classifier with Subtractive clustering method is utilized for EEG signals classifications. The suggested methods are applied to BCI Competition III dataset IVa and IVb and BCI Competition II dataset III.

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