Feature selection using regularized neighbourhood component analysis to enhance the classification performance of motor imagery signals

Malan, Nitesh Singh; Sharma, Shiru

doi:10.1016/j.compbiomed.2019.02.009

Cited by 108 publications

(36 citation statements)

References 21 publications

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“…They achieved a 94.4% accuracy. In [ 82 ], feature selection is developed based on a genetic algorithm using regularized neighborhood component analysis to enhance the motor imagery signal’s classification performance. The system achieved a 78.9% accuracy on average.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Feature Selection Model based on EEG Signals for Assessing the Cognitive Workload in Drivers

Becerra-Sánchez

Reyes-Muñoz

Ibáñez

2020

Sensors

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In recent years, research has focused on generating mechanisms to assess the levels of subjects’ cognitive workload when performing various activities that demand high concentration levels, such as driving a vehicle. These mechanisms have implemented several tools for analyzing the cognitive workload, and electroencephalographic (EEG) signals have been most frequently used due to their high precision. However, one of the main challenges in implementing the EEG signals is finding appropriate information for identifying cognitive states. Here, we present a new feature selection model for pattern recognition using information from EEG signals based on machine learning techniques called GALoRIS. GALoRIS combines Genetic Algorithms and Logistic Regression to create a new fitness function that identifies and selects the critical EEG features that contribute to recognizing high and low cognitive workloads and structures a new dataset capable of optimizing the model’s predictive process. We found that GALoRIS identifies data related to high and low cognitive workloads of subjects while driving a vehicle using information extracted from multiple EEG signals, reducing the original dataset by more than 50% and maximizing the model’s predictive capacity, achieving a precision rate greater than 90%.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Feature Selection Model based on EEG Signals for Assessing the Cognitive Workload in Drivers

Becerra-Sánchez

Reyes-Muñoz

Ibáñez

2020

Sensors

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“…This technique is based on a common orthogonal-wavelet-based decomposition method known as DWT. It is further improved to overcome DWT's aliasing and power-loss issues [5]. To implement DTCWT, two DWTs are operated in parallel to calculate the real and imaginary parts of the transform separately.…”

Section: Feature Extraction 421 Methods 1: Dtcwtmentioning

confidence: 99%

“…Extensive work has been done in the past in order to extract features that best represent MI signals. The work in [5] proposed the use of dual-tree complex wavelet transform (DTCWT) for signal decomposition as it overcame the problems of discrete wavelet transform (DWT) such as aliasing and power losses at the transaction bands, and the work in [6] compared several methods like wavelet packet decomposition (WPD), empirical mode decomposition (EMD), and DWT, and concluded that WPD was the superior method because it decomposed both the low and high frequency bands, creating a fine separation of relevant frequency bands. The study in [7] explored another implementation of WPD where features were extracted using a filter based on the Fisher criterion.…”

Section: Literature Surveymentioning

confidence: 99%

EEG-Controlled Robot Navigation using Hjorth Parameters and Welch-PSD

Francis¹,

Keran²,

Chetan³

et al. 2021

IJIES

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This study aimed to find an optimal feature-extraction method for real-time electroencephalography-based navigation. A comparative study of promising methods was done on two-class motor-imagery data. A novel combination of Hjorth parameters and Welch's-power-spectral-density produced the highest two-class accuracy of 0.84 across five subjects. The method is computationally inexpensive and provides short training and execution times that best suit real-time applications. The method yielded an average accuracy of 0.66 on three-class data. The real-time feasibility of the method was tested by implementing an object-retrieving robot and running a navigation experiment. The robot successfully navigated a three-circuit maze using three commandsright, left, and forwardand automatically retrieved the desired object. Along with being ideal for real-time applications, this method utilized only eight electrodes, making it compatible with cheap, portable BCI devices. The method and the application-framework can find commercial utility in the field of healthcare for the elderly and physically-handicapped.

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“…The EEG method is the most commonly used method as the signal collection method in MI-BMI systems due to its easy applicability and non-invasiveness. In studies using MI-BMI systems, mu (8-13 Hz) and beta (13-30 Hz) rhythms are widely researched due to their high temporal resolution and ability to identify mental tasks associated with different movement [3].…”

Section: Introductionmentioning

confidence: 99%

The Role of Feature Selection in Significant Information Extraction from EEG Signals

Dagdevir¹,

Tokmakçı²

2021

International Scientific and Vocational Studies Journal

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Information extraction from EEG signals for use in Brain Machine Interface systems has been a highly effective research topic recently. Due to the complexity, high dimension, and subject specific behavior of the EEG signals make feature extraction and selection very important. For this reason, there are many studies in the direction of feature extraction and selection which affect the performance of the Brain Machine Interface system at a high level. In this study, different statistical characteristics were obtained from wavelet coefficients obtained by wavelet transform by using BCI Competition IV-2b data set. The selection of the efficient ones of these features is provided by Principal Component Analysis. The fitness of logistic regression model established with both feature groups was measured by Akaike Information Criteria. The results indicated that relatively better statistical performance can be obtained by using fewer features thanks to PCA. These results are important in terms of statistical comparison and demonstration of the success in extracting information from EEG signals.

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Feature selection using regularized neighbourhood component analysis to enhance the classification performance of motor imagery signals

Cited by 108 publications

References 21 publications

Feature Selection Model based on EEG Signals for Assessing the Cognitive Workload in Drivers

Feature Selection Model based on EEG Signals for Assessing the Cognitive Workload in Drivers

EEG-Controlled Robot Navigation using Hjorth Parameters and Welch-PSD

The Role of Feature Selection in Significant Information Extraction from EEG Signals

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