Quaternion-Based Signal Analysis for Motor Imagery Classification from Electroencephalographic Signals

Batres-Mendoza, Patricia; Montoro-Sanjose, Carlos R.; Guerra-Hernandez, Erick Israel; Almanza-Ojeda, Dora-Luz; Rostro-González, Horacio; Romero-Troncoso, René de Jesús; Ibarra-Manzano, Mario Alberto

doi:10.3390/s16030336

Cited by 23 publications

(20 citation statements)

References 39 publications

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“…EEG signals are processed using power spectral density (PSD) to extract the most representative features in the context of cognitive workload. These characteristics are defined as frequency bands: Delta (0.5-4 Hz), Theta (4-8 Hz), Alpha (8-12 Hz ), Beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and Gamma (30-100 Hz) [23].…”

Section: Methodsmentioning

confidence: 99%

“…The classifier obtained 75% and 71.21% performance and presented problems identifying negative emotions. In [13], the authors present a quaternion-based signal analysis technique based on EEG signals to extract the registered cognitive activity features. The model achieved 86.44% accuracy and required a minimum limit of samples to obtain better results, increasing analysis, and information processing time.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Becerra-Sánchez¹,

Reyes²,

Ibáñez³

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Based on the expanded Euler's formula, the rotation for quaternion around the axis n = [ nx , ny , nz ] by angle theta is defined as follows (see [ 21 , 45 ] for further details).…”

Section: Methodsmentioning

confidence: 99%

“…On the other hand, in [ 21 ], the design of a motor imagery experiment was reported based on three mental processes: arrow moving to the left, arrow moving to the right, and waiting time, lasting 5 seconds per image. This study analyzed EEG signals with the implementation of the quaternion-based signal analysis (QSA).…”

Section: Introductionmentioning

confidence: 99%

Improving EEG-Based Motor Imagery Classification for Real-Time Applications Using the QSA Method

Batres-Mendoza

Ibarra-Manzano

Guerra-Hernandez

et al. 2017

Computational Intelligence and Neuroscience

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We present an improvement to the quaternion-based signal analysis (QSA) technique to extract electroencephalography (EEG) signal features with a view to developing real-time applications, particularly in motor imagery (IM) cognitive processes. The proposed methodology (iQSA, improved QSA) extracts features such as the average, variance, homogeneity, and contrast of EEG signals related to motor imagery in a more efficient manner (i.e., by reducing the number of samples needed to classify the signal and improving the classification percentage) compared to the original QSA technique. Specifically, we can sample the signal in variable time periods (from 0.5 s to 3 s, in half-a-second intervals) to determine the relationship between the number of samples and their effectiveness in classifying signals. In addition, to strengthen the classification process a number of boosting-technique-based decision trees were implemented. The results show an 82.30% accuracy rate for 0.5 s samples and 73.16% for 3 s samples. This is a significant improvement compared to the original QSA technique that offered results from 33.31% to 40.82% without sampling window and from 33.44% to 41.07% with sampling window, respectively. We can thus conclude that iQSA is better suited to develop real-time applications.

show abstract

“…The classifier obtains a 75% and 71.21% performance accuracy and presents problems associated with identifying negative emotions. In [ 20 ], the authors present a quaternion-based signal analysis technique based on EEG signals to extract the registered cognitive activity features. The model achieves an 86.44% accuracy and requires a minimum limit of samples to obtain better results, increasing the analysis and information processing time.…”

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

View full text Add to dashboard Cite

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%.

show abstract

Quaternion-Based Signal Analysis for Motor Imagery Classification from Electroencephalographic Signals

Cited by 23 publications

References 39 publications

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

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

Improving EEG-Based Motor Imagery Classification for Real-Time Applications Using the QSA Method

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

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