Performance Analysis of Machine Learning Algorithms for Classifying Hand Motion-Based EEG Brain Signals

Altameem, Ayman; Sachdev, Jaideep Singh; Singh, Vijander; Poonia, Ramesh Chandra; Kumar, Sandeep; Saudagar, Abdul Khader Jilani

doi:10.32604/csse.2022.023256

Cited by 6 publications

(2 citation statements)

References 15 publications

(10 reference statements)

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“…Nevertheless, we changed the method of EEG feature extraction proposed in this paper to improve the prediction accuracy of the model. Altameem et al (2022) is the application of EEG in brain computer interface motor imagery (BCI-MI), and their research shows that XGBoost using FFT features in EEG achieves 88% accuracy [37]. Although the above studies come from different fields, it also indicates that our study has certain novelty and effectiveness compared with the current mainstream methods.…”

Section: Resultsmentioning

confidence: 84%

EEG recognition model based on wavelet packet decomposition and ConvNet for art design decisions

et al. 2023

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Aiming at the subjective and unquantifiable problems in art design decisions, using EEG (electroencephalogram) to explain individual behavior is an effective solution. However, the accuracy of the traditional method is lower because of the complexity of EEG. Therefore, we propose the WPD-ConvNet recognition model based on wavelet packet decomposition and convolutional networks to enhance the recognition accuracy of EEG. First, subjects wear brain electrode caps to watch four art designs with different styles and need to make decisions. The EEG is collected during this process. Then, we use the WPD to obtain the features of EEG signals from the decomposed optimal subband nodes. Compared with FFT, WPD divides the frequency band through multiple levels to obtain more EEG time-frequency features related to art design decisions. Finally, we construct an convolutional neural network to identify these features. The experimental results show that our proposed EEG recognition model can achieve an accuracy of 88.7%, which is about 10% higher than others.

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Section: Resultsmentioning

confidence: 84%

EEG recognition model based on wavelet packet decomposition and ConvNet for art design decisions

et al. 2023

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“…However, due to the mechanics of matrix convolutions in the CNNs, the convolutions are carried out over the entire scalogram in the first stage as well as the subsequent convolution stages. Features extracted from the scalogram are also combined into feature vectors for input to many other classifiers such as support vector machines (SVMs) [7,[12][13][14][15], random forests (RFs) [16,17], k-nearest neighbor (k-NN) [15,18,19], and multilayer perceptron (MLP) neural networks [20].…”

Section: Introductionmentioning

confidence: 99%

Exploiting the Cone of Influence for Improving the Performance of Wavelet Transform-Based Models for ERP/EEG Classification

Chen

Gupta

2022

Brain Sciences

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Features extracted from the wavelet transform coefficient matrix are widely used in the design of machine learning models to classify event-related potential (ERP) and electroencephalography (EEG) signals in a wide range of brain activity research and clinical studies. This novel study is aimed at dramatically improving the performance of such wavelet-based classifiers by exploiting information offered by the cone of influence (COI) of the continuous wavelet transform (CWT). The COI is a boundary that is superimposed on the wavelet scalogram to delineate the coefficients that are accurate from those that are inaccurate due to edge effects. The features derived from the inaccurate coefficients are, therefore, unreliable. In this study, it is hypothesized that the classifier performance would improve if unreliable features, which are outside the COI, are zeroed out, and the performance would improve even further if those features are cropped out completely. The entire, zeroed out, and cropped scalograms are referred to as the “same” (S)-scalogram, “zeroed out” (Z)-scalogram, and the “valid” (V)-scalogram, respectively. The strategy to validate the hypotheses is to formulate three classification approaches in which the feature vectors are extracted from the (a) S-scalogram in the standard manner, (b) Z-scalogram, and (c) V-scalogram. A subsampling strategy is developed to generate small-sample ERP ensembles to enable customized classifier design for single subjects, and a strategy is developed to select a subset of channels from multiple ERP channels. The three scalogram approaches are implemented using support vector machines, random forests, k-nearest neighbor, multilayer perceptron neural networks, and deep learning convolution neural networks. In order to validate the performance hypotheses, experiments are designed to classify the multi-channel ERPs of five subjects engaged in distinguishing between synonymous and non-synonymous word pairs. The results confirm that the classifiers using the Z-scalogram features outperform those using the S-scalogram features, and the classifiers using the V-scalogram features outperform those using the Z-scalogram features. Most importantly, the relative improvement of the V-scalogram classifiers over the standard S-scalogram classifiers is dramatic. Additionally, enabling the design of customized classifiers for individual subjects is an important contribution to ERP/EEG-based studies and diagnoses of patient-specific disorders.

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Exploration and Analysis of Seizure Spikes Through Spectral Domain Transformation

Najmusseher,

Nizar Banu,

Azar

et al. 2024

Smart Innovation, Systems and Technologies

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Performance Analysis of Machine Learning Algorithms for Classifying Hand Motion-Based EEG Brain Signals

Cited by 6 publications

References 15 publications

EEG recognition model based on wavelet packet decomposition and ConvNet for art design decisions

EEG recognition model based on wavelet packet decomposition and ConvNet for art design decisions

Exploiting the Cone of Influence for Improving the Performance of Wavelet Transform-Based Models for ERP/EEG Classification

Exploration and Analysis of Seizure Spikes Through Spectral Domain Transformation

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