Classification of Upper Arm Movements from EEG signals using Machine Learning with ICA Analysis

Kokate, Pranali; Pancholi, Sidharth; Joshi, Amit M.

doi:10.48550/arxiv.2107.08514

Cited by 6 publications

(7 citation statements)

References 29 publications

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“…Many studies were involved for the segmentation of such EEG into frequency bands for performance analysis. In this study, EEG is divided into five bands using butterworth bandpass filter: theta (4-8Hz), alpha (8-15 Hz), beta , gamma (32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45), and all bands (4-45 Hz). Thereafter, analysis is done to measure the model performance in Sz detection over various frequency bands.…”

Section: Methodsmentioning

confidence: 99%

“…In recent years, there has been a major boost in deep learning-based techniques for the classification of EEG signals [43]. Researchers have explored both the spatial and temporal information out of EEG through deep learning architectures [44]. CNN models have shown remarkable results in extracting meaningful features or patterns from images.…”

Section: Deep Learning Approach To Eegmentioning

confidence: 99%

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Novel EEG based Schizophrenia Detection with IoMT Framework for Smart Healthcare

Sharma¹,

Joshi²

2021

Preprint

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In the field of neuroscience, Brain activity analysis is always considered as an important area. Schizophrenia(Sz) is a brain disorder that severely affects the thinking, behavior, and feelings of people all around the world. Electroencephalography (EEG) is proved to be an efficient biomarker in Sz detection. EEG is a non-linear time-series signal and utilizing it for investigation is rather crucial due to its non-linear structure. This paper aims to improve the performance of EEG based Sz detection using a deep learning approach. A novel hybrid deep learning model known as SzHNN (Schizophrenia Hybrid Neural Network), a combination of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) has been proposed. CNN network is used for local feature extraction and LSTM has been utilized for classification. The proposed model has been compared with CNN only, LSTM only, and machine learning based models. All the models have been evaluated on two different datasets wherein Dataset 1 consists of 19 subjects and Dataset 2 consists of 16 subjects. Several experiments have been conducted for the same using various parametric settings on different frequency bands and using different sets of electrodes on the scalp. Based on all the experiments, it is evident that the proposed hybrid model (SzHNN) provides the highest classification accuracy of 99.9% in comparison to other existing models. The proposed model overcomes the influence of different frequency bands and even showed a much better accuracy of 91% with only 5 electrodes. The proposed model is also evaluated in Internet of Medical Things (IoMT) framework for smart healthcare and remote monitoring applications.

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Section: Methodsmentioning

confidence: 99%

Section: Deep Learning Approach To Eegmentioning

confidence: 99%

Novel EEG based Schizophrenia Detection with IoMT Framework for Smart Healthcare

Sharma¹,

Joshi²

2021

Preprint

Self Cite

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“…This study extracted six common time-domain features for gesture recognition: standard deviation, pulse factor, kurtosis, skewness, margin factor, and peak-to-peak value [43][44][45]. To streamline the computation, PCA was applied to these five-dimensional features for each finger, reducing them to the two most significant eigendimensions.…”

Section: Clustering Analysis Of Featuresmentioning

confidence: 99%

Real-time continuous gesture recognition system based on PSO-PNN

Ren,

Gao,

et al. 2024

Meas. Sci. Technol.

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In the field of Natural Human-Machine Interaction (NHMI), wearable gesture interaction technologies have received considerable attention, particularly Continuous Gesture Recognition (CG). However, CG faces several challenges, including the impact of motion characteristics on gesture recognition and performance that is not sufficiently robust. Traditional algorithms are highly dependent on samples, thus meeting the requirements of low sample volume and high accuracy simultaneously is challenging. To address these challenges, we propose a real-time continuous gesture recognition system based on Particle Swarm Optimization (PSO) and Probabilistic Neural Network (PNN). This system employs Principal Component Analysis (PCA) for signal dimensionality reduction to alleviate computational burden and uses K-means clustering and Pearson Correlation Coefficient (PCC) to extract optimal features for gesture classification. In offline gesture recognition experiments involving six continuous gestures, the algorithm achieved an accuracy rate of 97% with a training set of 300 samples and a runtime of just 31.25ms. Compared to other five algorithms, the proposed algorithm improved accuracy by at least 9% and reduced the runtime by 40.475ms. Moreover, gesture recognition experiments were conducted using different datasets, with the PSO-PNN algorithm achieving an average recognition rate of 90.17%, at least 9.84% higher than other algorithms. Finally, in experiments on online continuous gesture control for robots in complex environments, the PSO-PNN demonstrated real-time performance of 28.56ms and a task completion rate of 90.67%, validating the feasibility of PSO-PNN. This research provides a substantial theoretical and technical foundation for the ongoing enhancement and application of continuous gesture recognition technology.

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“…Hence, there has been conscious efforts are going on for next generation of myelectronic prosthesis control with multi-model approach with minimum usage of sensors. The prosthetic control can be done using EMG or EEG signal [17]. There are various Brain Computer Interface application were developed using EEG.…”

Section: Related Workmentioning

confidence: 99%

Novel Time Domain Based Upper-Limb Prosthesis Control using Incremental Learning Approach

Pancholi¹,

Joshi²

2021

Preprint

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The upper limb of the body is a vital for various kind of activities for human. The complete or partial loss of the upper limb would lead to a significant impact on daily activities of the amputees. EMG carries important information of human physique which helps to decode the various functionalities of human arm. EMG signal based bionics and prosthesis have gained huge research attention over the past decade. Conventional EMG-PR based prosthesis struggles to give accurate performance due to off-line training used and incapability to compensate for electrode position shift and change in arm position. This work proposes online training and incremental learning based system for upper limb prosthetic application. This system consists of ADS1298 as AFE (analog front end) and a 32 bit arm cortex-m4 processor for DSP (digital signal processing). The system has been tested for both intact and amputated subjects. Time derivative moment based features have been implemented and utilized for effective pattern classification. Initially, system have been trained for four classes using the on-line training process later on the number of classes have been incremented on user demand till eleven, and system performance has been evaluated. The system yielded a completion rate of 100% for healthy and amputated subjects when four motions have been considered. Further 94.33% and 92% completion rate have been showcased by the system when the number of classes increased to eleven for healthy and amputees respectively. The motion efficacy test is also evaluated for all the subjects. The highest efficacy rate of 91.23% and 88.64% are observed for intact and amputated subjects respectively.

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Classification of Upper Arm Movements from EEG signals using Machine Learning with ICA Analysis

Cited by 6 publications

References 29 publications

Novel EEG based Schizophrenia Detection with IoMT Framework for Smart Healthcare

Novel EEG based Schizophrenia Detection with IoMT Framework for Smart Healthcare

Real-time continuous gesture recognition system based on PSO-PNN

Novel Time Domain Based Upper-Limb Prosthesis Control using Incremental Learning Approach

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