Frequency Domain Approach in CSP based Feature Extraction for EEG Signal Classification

Saha, Pritom Kumar; Rahman, Md. Asadur; Mollah, Md. Nurunnabi

doi:10.1109/ecace.2019.8679463

Cited by 7 publications

(12 citation statements)

References 17 publications

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“…This procedure has been given in along with the classification accuracy, the sensitivity and specificity were also calculated for the two-class data. The sensitivity and specificity were calculated as follows [4]: Here, TP and TN stand for true positive and true negative, FP and FN stand for false positive and false negative.…”

Section: Classificationmentioning

confidence: 99%

“…Any psychophysiological change in the human body is directly related to the change in the cortical neuroelectric potential. Electroencephalography (EEG) is a data acquisition technique that measures this neuroelectric potential which is a communication pattern of the neurons [4]. Numerous established systems and sophisticated research are going on to extract detail and significant information from the EEG signals.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the significant variance between two classes of EEG signals in the temporal domain can be a cause of over-fitting [24]. Besides, conventional CSP tends to increase the class-to-class discrimination in time domain properties whereas the EEG signal shows a very short period (generally some millisecond) time domain property [4]. Besides, the EEG signal on the temporal domain is not impregnable to noise sensitivity and as a consequence attempt to prevent the influence of deep-rooted intensified noise has been proven higher efficacious work [25].…”

Section: Introductionmentioning

confidence: 99%

“…Quantification of the strength is followed by performing power spectral density (PSD) that will provide a chance of redemption from noise sensitivity and computational complexity. A pilot analytical study based on two-class motor imagery EEG data was conducted in [4] and found some promising results. To validate this novel idea, we aim to apply it to some more complex and widely acceptable environments.…”

Section: Introductionmentioning

confidence: 99%

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Common Spatial Pattern in Frequency Domain for Feature Extraction and Classification of Multichannel EEG Signals

Saha

Rahman²,

Alam

et al. 2021

SN COMPUT. SCI.

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The extraction methodology of the significant features from the signals is one of the most important prerequisite steps for EEG signal classification. Common spatial pattern (CSP) is a widely used feature extraction method for EEG signal but with a lacking of failing to maintain discriminative features between classes in the time domain, and further as a consequence, ends up in inconvenience with erroneous output. To overcome the limitations of the convention CSP, this research work proposes a novel frequency domain CSP (FCSP) method for feature extraction. This method proposes to convert the time domain EEG signal to its power spectral density (PSD) so that the event-related variation can be found in the frequency domain. After that, the CSP method is applied to the PSD values of the selected channels to extract the variation based on the spatial pattern of the channels for the events. The output of this method helps to extract simple features from the FCSP-PSD data for the classification. The proposed method is applied to motor imagery data from BCI competition IV. To check the applicability of the proposed method, a complex environment was created considering the same lobe events such as combined left and right feet (Class#1) versus right-hand (Class#2) imagery movement. To compare the performance of the proposed work, the method is also applied to the conventional classification problem (left-hand vs right-hand imagery movement) and found very promising results of 91% accuracy on average.

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Section: Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Common Spatial Pattern in Frequency Domain for Feature Extraction and Classification of Multichannel EEG Signals

Saha

Rahman²,

Alam

et al. 2021

SN COMPUT. SCI.

Self Cite

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“…www.ijacsa.thesai.org A number of feature extraction methods from EEG signal have been proposed in recent years. Within these feature extraction techniques, there are autoregressive (AR) methods [16,17], wavelet transforms (WT) method [17][18][19][20][21], and phasespace reconstruction approach [10], CSP based methods [13,14,22], empirical mode decomposition [23][24][25], etc. For a wide range of pattern recognition, wavelet packet transformation (WPT) provides excellent time-frequency features.…”

Section: Introductionmentioning

confidence: 99%

Four-Class Motor Imagery EEG Signal Classification using PCA, Wavelet and Two-Stage Neural Network

Rahman¹,

Khanam²,

Kazem³

et al. 2019

IJACSA

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Electroencephalogram (EEG) is the most significant signal for brain-computer interfaces (BCI). Nowadays, motor imagery (MI) movement based BCI is highly accepted method for. This paper proposes a novel method based on the combined utilization of principal component analysis (PCA), wavelet packet transformation (WPT), and two-stage machine learning algorithm to classify four-class MI EEG signal. This work includes four-class MI events by an imaginary lifting of the left hand, right hand, left foot, and Right Foot. The main challenge of this work is to discriminate the similar lobe EEG signal pattern such as left foot VS left hand. Another critical problem is to identify the MI movements of two different feet because their activation level is very low and show an almost similar pattern. This work firstly uses the PCA to reduce the signal dimensions of the left and right lobe of the brain. Then, WPT is used to extract the feature from the different class EEG signal. Finally, the artificial neural network is trained into two stages-1 st stage identifies the lobe from the signal pattern and the 2 nd stage identifies whether the signal is of MI hand or MI foot movement. The proposed method is applied to the 4-class MI movement related EEG signals of 15 participants and found excellent classification accuracy (>74% on average). The outcomes of the proposed method prove its effectiveness in practical BCI implementation.

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Object Weight Perception in Motor Imagery Using Fourier-Based Synchrosqueezing Transform and Regularized Common Spatial Patterns

Karakullukcu,

Altindış,

Yilmaz

2024

IEEE Access

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This study addresses the challenge faced by individuals with upper-limb prostheses in regulating grip force and adapting movements to different object weights. Despite limited exploration, this research pioneers the use of EEG to estimate object weight perception in the context of upper-limb prostheses. Investigating neural correlates in this population provides valuable insights and aids the development of neurofeedback-based strategies for weight perception. Our objective is to identify EEG features predicting the weight perception of held objects. Employing Fourier-based synchrosqueezing transform (FSST) and regularized Common Spatial Patterns (CSP) features, we classify motor imagery waves representing three weight categories (light, medium, heavy). Subjects perform actual motor tasks before imagery sessions, and our approach integrates EEG features of both movements to train subject-specific machine learning models. Results reveal that FSST-singular value decomposition (SVD) features for medium and heavy objects are most distinctive. Achieving up to 90% accuracy, spatial features demonstrate effective classification of motor imagery for different weights. Unlike weight prediction studies, our focus is on visual perception and imagination of object weights, enhancing prosthetic hand system preconditioning. Binary classification surpasses 70% accuracy in predicting object weights, uniquely utilizing actual movement data for CSP algorithm regularization coefficient estimation.INDEX TERMS Brain computer interfaces, common spatial pattern (CSP), EEG signal processing, Fourierbased synchrosqueezing transform (FSST), weight perception.

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Frequency Domain Approach in CSP based Feature Extraction for EEG Signal Classification

Cited by 7 publications

References 17 publications

Common Spatial Pattern in Frequency Domain for Feature Extraction and Classification of Multichannel EEG Signals

Common Spatial Pattern in Frequency Domain for Feature Extraction and Classification of Multichannel EEG Signals

Four-Class Motor Imagery EEG Signal Classification using PCA, Wavelet and Two-Stage Neural Network

Object Weight Perception in Motor Imagery Using Fourier-Based Synchrosqueezing Transform and Regularized Common Spatial Patterns

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