Classification of Mental Workload Levels by Using EEG Signals

Aydın, Eda Akman

doi:10.2339/politeknik.794655

Cited by 2 publications

(2 citation statements)

References 30 publications

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“…FD algorithms try to determine the number of times a pattern is repeated in time series and in this way measure the self-similarity of the content in the signals and reflects its complexity [25]. FDs are used in the diagnosis of diseases such as autism, epilepsy, schizophrenia, and Alzheimer's, as well as determining mental workload levels, emotional and cognitive changes by using EEG signals [26,27]. On the other hand, it has also been used for feature extraction in BCI systems based on the use of EEG and fNIRS signals [27,28].…”

Section: Katz's Fractal Dimension (Kfd)mentioning

confidence: 99%

“…FDs are used in the diagnosis of diseases such as autism, epilepsy, schizophrenia, and Alzheimer's, as well as determining mental workload levels, emotional and cognitive changes by using EEG signals [26,27]. On the other hand, it has also been used for feature extraction in BCI systems based on the use of EEG and fNIRS signals [27,28].…”

Section: Katz's Fractal Dimension (Kfd)mentioning

confidence: 99%

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Detection of Movement Related Cortical Potentials from Single Trial EEG Signals

Aydın

2023

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım Ve Teknoloji

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Movement-Related Cortical Potentials (MRCP) are signals that begin to appear approximately two seconds before the onset of voluntary movements and can be recorded with EEG. MRCP is an important sign that the movement will begin. Determining the movement intention before the action is extremely important information especially for real-time BCI systems. By using MRCP, Brain-Computer Interface (BCI) users' movement intention can be determined prior to the move and this sign can be used as a control signal. In this study, it was aimed to determine the movement and resting states with high accuracy with MRCP signals. Furthermore, the effects of filter cutoff frequencies, number of electrodes, and MRCP time interval window on the success of distinguishing movement/resting states in the preprocessing stage were investigated. For this purpose, Katz fractal dimension and nonlinear support vector machine methods were used in the feature extraction and classification stages, respectively. The proposed method was tested on the attempted hand and arm movements dataset containing EEG signals of 10 participants with spinal cord injury. Katz fractal dimension and support vector machines methods can determine movement and resting states with an average of 96.47% accuracy using MRCP signals. If the number of electrodes to be used in signal analysis was 3, 9 and 61, the obtained accuracy rates were determined as 83.71%, 90.67%, and 96.47%, respectively. The experimental results also showed that the filter cutoff frequencies used in the preprocessing had a significant effect on the accuracy.

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Section: Katz's Fractal Dimension (Kfd)mentioning

confidence: 99%

Section: Katz's Fractal Dimension (Kfd)mentioning

confidence: 99%

Detection of Movement Related Cortical Potentials from Single Trial EEG Signals

Aydın

2023

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım Ve Teknoloji

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Classification Mental Workload Levels from EEG Signals with 1D Convolutional Neural Network

Baydemir

Latifoğlu

Orhanbulucu

2022

EJRnD

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Mental workload (MWL) can be estimated according to the state of cognitive capacity after an activity. In this study, it is aimed to classify MWL levels from Electroencephalogram (EEG) signals recorded from a task moment. Using the proposed one-dimensional convolutional neural network (1D-CNN) model in the study, low (L) and high (H) level WL states were classified. The classification process was carried out in two stages. EEG signals passed through the preprocessing stage were classified with 1D-CNN in the first stage. In the second step, these signals were decomposed into subbands by applying Empirical Mode Decomposition (EMD) and classified with 1D-CNN. As a result of the classification process, accuracy (Acc), sensitivity (Sens), and specificity (Spe) values were obtained and evaluated in this study. As a result of the evaluation, the most successful Acc rate was 98.4%, Sens rate 97.62%, and Spe rate 98.94%

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Classification of Mental Workload Levels by Using EEG Signals

Cited by 2 publications

References 30 publications

Detection of Movement Related Cortical Potentials from Single Trial EEG Signals

Detection of Movement Related Cortical Potentials from Single Trial EEG Signals

Classification Mental Workload Levels from EEG Signals with 1D Convolutional Neural Network

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