Envelope Extraction Algorithms and the Application in Motor Imagery

2013 ◽  
Vol 401-403 ◽  
pp. 1551-1554
Author(s):  
Xiao Xiao Gong ◽  
Xiao Pei Wu ◽  
Xiao Jing Guo ◽  
Lei Zhang
Keyword(s):  
Motor Imagery ◽  
Hilbert Transform ◽  
Sliding Window ◽  
Signal Denoising ◽  
Eeg Signals ◽  
Envelope Detection ◽  
Application Potential ◽  
Left And Right ◽  
Envelope Extraction ◽  
Eeg Signal Processing

The envelope information is an important feature in EEG signal processing. This paper analyses the application of Hilbert transform and sliding window Infomax algorithm in envelope detection of EEG signals from different respects, and compares the classification results in left and right hand motor imagery. From the results, we see that, the signal envelope extracted by Hilbert transform is more accurate than sliding window Infomax algorithm, but the latter is superior in signal denoising, real-time ability and the richness of characteristics, sliding window Infomax algorithm has greater application potential.

Characterization of Motor Imagery based EEG signals with Hilbert Transform

2021 ◽  
Author(s):  
Umar Farooq Malik ◽  
Nasir Rashid ◽  
Rabia Avais Khan ◽  
Muhammad Shahzaib ◽  
Arshia Arif ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Hilbert Transform ◽  
Eeg Signals

Research on the Physical Mechanism of Motor Imagery

2011 ◽  
Vol 66-68 ◽  
pp. 279-283
Author(s):  
Zhen Dong Mu ◽  
Jin Li Wang
Keyword(s):  
Feature Extraction ◽  
Motor Imagery ◽  
Time Domain ◽  
Brain Mapping ◽  
Physical Mechanism ◽  
Eeg Analysis ◽  
Imaginary Frequency ◽  
Eeg Signals ◽  
Mapping Analysis ◽  
Left And Right

The motor imagery to be widely used in BCI systems, the traditional focus on EEG analysis in feature extraction and classification, this paper of EEG from the left and right imaginary frequency domain, time domain and brain mapping analysis on the EEG, to analyze the characteristics of EEG signals about imagination.

scholarly journals Motor Imagery Classification using Wavelet-Based Features and Tensorflow

2019 ◽  
Vol 9 (1) ◽  
pp. 4650-4652
Keyword(s):  
Frequency Distribution ◽  
Motor Imagery ◽  
Wavelet Transforms ◽  
Haar Wavelet ◽  
Second Step ◽  
Eeg Signals ◽  
Accuracy Rate ◽  
Motion Imagery ◽  
Left And Right ◽  
Electroencephalogram Eeg

This paper proposes a methodology for making a decision on left and right motor imagery using Tensorflow and wavelet-based feature extraction. Wavelet coefficients are extracted by the Haar wavelet transforms from electroencephalogram (EEG) signals in the first step. In the second step, 60 wavelet-based features are extracted by the frequency distribution and the amount of variability in frequency distribution. In the final step, this paper classified left or right motion imagery using these 60 features as inputs to the Tensorflow. The proposed methodology shows that the performance result is 82.14% with 60 features in accuracy rate

scholarly journals A Novel Simplified Convolutional Neural Network Classification Algorithm of Motor Imagery EEG Signals Based on Deep Learning

2020 ◽  
Vol 10 (5) ◽  
pp. 1605 ◽  
Author(s):  
Feng Li ◽  
Fan He ◽  
Fei Wang ◽  
Dengyong Zhang ◽  
Yi Xia ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Motor Imagery ◽  
Recognition Rate ◽  
Classification Performance ◽  
Eeg Signals ◽  
Time Frequency ◽  
Right Hand ◽  
Left And Right

Left and right hand motor imagery electroencephalogram (MI-EEG) signals are widely used in brain-computer interface (BCI) systems to identify a participant intent in controlling external devices. However, due to a series of reasons, including low signal-to-noise ratios, there are great challenges for efficient motor imagery classification. The recognition of left and right hand MI-EEG signals is vital for the application of BCI systems. Recently, the method of deep learning has been successfully applied in pattern recognition and other fields. However, there are few effective deep learning algorithms applied to BCI systems, particularly for MI based BCI. In this paper, we propose an algorithm that combines continuous wavelet transform (CWT) and a simplified convolutional neural network (SCNN) to improve the recognition rate of MI-EEG signals. Using the CWT, the MI-EEG signals are mapped to time-frequency image signals. Then the image signals are input into the SCNN to extract the features and classify them. Tested by the BCI Competition IV Dataset 2b, the experimental results show that the average classification accuracy of the nine subjects is 83.2%, and the mean kappa value is 0.651, which is 11.9% higher than that of the champion in the BCI Competition IV. Compared with other algorithms, the proposed CWT-SCNN algorithm has a better classification performance and a shorter training time. Therefore, this algorithm could enhance the classification performance of MI based BCI and be applied in real-time BCI systems for use by disabled people.

scholarly journals Motor Imagery Classification Using Mu and Beta Rhythms of EEG with Strong Uncorrelating Transform Based Complex Common Spatial Patterns

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Youngjoo Kim ◽  
Jiwoo Ryu ◽  
Ko Keun Kim ◽  
Clive C. Took ◽  
Danilo P. Mandic ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Spatial Patterns ◽  
Supplementary Information ◽  
Eeg Signals ◽  
Common Spatial Patterns ◽  
Right Hand ◽  
Power Difference ◽  
Left And Right ◽  
Beta Rhythms

Recent studies have demonstrated the disassociation between the mu and beta rhythms of electroencephalogram (EEG) during motor imagery tasks. The proposed algorithm in this paper uses a fully data-driven multivariate empirical mode decomposition (MEMD) in order to obtain the mu and beta rhythms from the nonlinear EEG signals. Then, the strong uncorrelating transform complex common spatial patterns (SUTCCSP) algorithm is applied to the rhythms so that the complex data, constructed with the mu and beta rhythms, becomes uncorrelated and its pseudocovariance provides supplementary power difference information between the two rhythms. The extracted features using SUTCCSP that maximize the interclass variances are classified using various classification algorithms for the separation of the left- and right-hand motor imagery EEG acquired from the Physionet database. This paper shows that the supplementary information of the power difference between mu and beta rhythms obtained using SUTCCSP provides an important feature for the classification of the left- and right-hand motor imagery tasks. In addition, MEMD is proved to be a preferred preprocessing method for the nonlinear and nonstationary EEG signals compared to the conventional IIR filtering. Finally, the random forest classifier yielded a high performance for the classification of the motor imagery tasks.

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

scholarly journals Implicit Motor Imagery and the Lateral Occipitotemporal Cortex: Hints for Tailoring Non-Invasive Brain Stimulation

2020 ◽  
Vol 17 (16) ◽  
pp. 5851 ◽  
Author(s):  
Massimiliano Conson ◽  
Roberta Cecere ◽  
Chiara Baiano ◽  
Francesco De Bellis ◽  
Gabriela Forgione ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Brain Stimulation ◽  
Repetitive Transcranial Magnetic Stimulation ◽  
Dominant Hand ◽  
Non Invasive ◽  
Implicit Motor ◽  
Left And Right ◽  
Occipitotemporal Cortex ◽  
Biomechanical Effect ◽  
Motor Representations

Background: Recent evidence has converged in showing that the lateral occipitotemporal cortex is over-recruited during implicit motor imagery in elderly and in patients with neurodegenerative disorders, such as Parkinson’s disease. These data suggest that when automatically imaging movements, individuals exploit neural resources in the visual areas to compensate for the decline in activating motor representations. Thus, the occipitotemporal cortex could represent a cortical target of non-invasive brain stimulation combined with cognitive training to enhance motor imagery performance. Here, we aimed at shedding light on the role of the left and right lateral occipitotemporal cortex in implicit motor imagery. Methods: We applied online, high-frequency, repetitive transcranial magnetic stimulation (rTMS) over the left and right lateral occipitotemporal cortex while healthy right-handers judged the laterality of hand images. Results: With respect to the sham condition, left hemisphere stimulation specifically reduced accuracy in judging the laterality of right-hand images. Instead, the hallmark of motor simulation, i.e., the biomechanical effect, was never influenced by rTMS. Conclusions: The lateral occipitotemporal cortex seems to be involved in mental representation of the dominant hand, at least in right-handers, but not in reactivating sensorimotor information during simulation. These findings provide useful hints for developing combined brain stimulation and behavioural trainings to improve motor imagery.

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