Filtering techniques for channel selection in motor imagery EEG applications: a survey

Baig, Muhammad Zeeshan; Aslam, Nauman; Shum, Hubert P. H.

doi:10.1007/s10462-019-09694-8

Cited by 132 publications

(79 citation statements)

References 85 publications

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“…In practice, extraction from fewer sensorimotor area is achieved in order to reduce the computational complexity without affecting the BCI system performance [ 45 ]. To this end, we select the EEG recordings measured over the sensorimotor area, evaluating two configurations of scalp positions: ( a ) narrow electrode arrangement (noted as 2Ch) that includes two channels (

): C3 (left motor cortical region) and C4 (right), ( b ) wide arrangement (6Ch) that holds six surrounding electrodes (

): C3 and P3 (left motor cortex), Cz and Pz (middle cortex), and C4 and P4 (right cortex).…”

Section: Experimental Set-upmentioning

confidence: 99%

Regression Networks for Neurophysiological Indicator Evaluation in Practicing Motor Imagery Tasks

et al. 2020

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Motor Imagery (MI) promotes motor learning in activities, like developing professional motor skills, sports gestures, and patient rehabilitation. However, up to 30% of users may not develop enough coordination skills after training sessions because of inter and intra-subject variability. Here, we develop a data-driven estimator, termed Deep Regression Network (DRN), which jointly extracts and performs the regression analysis in order to assess the efficiency of the individual brain networks in practicing MI tasks. The proposed double-stage estimator initially learns a pool of deep patterns, extracted from the input data, in order to feed a neural regression model, allowing for infering the distinctiveness between subject assemblies having similar variability. The results, which were obtained on real-world MI data, prove that the DRN estimator fosters pre-training neural desynchronization and initial training synchronization to predict the bi-class accuracy response, thus providing a better understanding of the Brain–Computer Interface inefficiency of subjects.

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): C3 (left motor cortical region) and C4 (right), ( b ) wide arrangement (6Ch) that holds six surrounding electrodes (

): C3 and P3 (left motor cortex), Cz and Pz (middle cortex), and C4 and P4 (right cortex).…”

Section: Experimental Set-upmentioning

confidence: 99%

Regression Networks for Neurophysiological Indicator Evaluation in Practicing Motor Imagery Tasks

et al. 2020

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“…EEG signals allow the behavior of a person’s brain activity to be analyzed in real-time. However, this type of physiological signal generates a lot of information per second, which increases proportionally according to the collection time and the number of sensor channels, consequently producing large volumes of data and resulting in complex and robust treatment [ 9 , 10 ].…”

Section: Introductionmentioning

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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“…EEG signals allow analyzing the behavior of a person's brain activity in real-time. However, this type of physiological signals generates so much information per second, which increases proportionally according to the collection time and the number of sensor channels, which produce large volumes of data resulting in complex and robust treatment [9,10].…”

Section: Introductionmentioning

confidence: 99%

Feature Selection Model based on EEG signals to Assess the Cognitive Workload in Drivers

Becerra-Sánchez¹,

Reyes²,

Ibáñez³

2020

Preprint

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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 to analyze cognitive workload where the electroencephalographic (EEG) signals are the most used due to its high precision. However, one of the main challenges in the EEG signals implementing is finding the appropriate information to identify cognitive states. Here we show 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 workload 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 workload of subjects while driving a vehicle using information extracted from multiple EEG signals, reducing the original dataset by more than 50%, maximizing the model's predictive capacity-achieving a precision rate greater than 90%.

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Filtering techniques for channel selection in motor imagery EEG applications: a survey

Cited by 132 publications

References 85 publications

Regression Networks for Neurophysiological Indicator Evaluation in Practicing Motor Imagery Tasks

Regression Networks for Neurophysiological Indicator Evaluation in Practicing Motor Imagery Tasks

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

Feature Selection Model based on EEG signals to Assess the Cognitive Workload in Drivers

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