Feature Selection Model based on EEG Signals for Assessing the Cognitive Workload in Drivers
Abstract
:1. Introduction
2. Methodology
2.1. Statistical Analysis
2.2. GALoRIS
2.3. Population
2.4. Fitness Function
2.5. Selection
2.6. Crossing
2.7. Mutation
2.8. Detection Rules
2.9. Information Structuring
2.10. Classifiers
2.11. Label
3. Experimentation and Materials
3.1. Design of the Experiment
- Baseline: The participant takes a seat and places the Emotiv EPOC sensor on their head [51]. The subject keeps their eyes closed and is acoustically isolated for 10 min, where the sensor is activated to collect information;
- First Task (Task_1): The participant starts driving the vehicle without any distraction. During driving, the EEG signals, ISA, and ER are collected. In the end, NASA-TLX is applied;
- Second task (Task_2): In order to increase the subject’s cognitive workload levels, the stress induction protocol proposed in [7] is applied as a second task. The task consists of the random mentioning of a series of digits that the participant has to repeat, following the order of the set of numbers given. All measurements are collected.
3.2. Subjective Measures
3.3. Measurement of the Vehicle Performance
3.4. Collection and Extraction of EEG Signals
3.5. Dataset and Parameters
4. Results
4.1. Subjective and Vehicle Performance Measures
4.2. EEG Signals
4.3. Statistical Test Results
4.4. Labeling Results
4.5. GALoRIS Results
4.6. Classifier Results
5. Conclusions and Discussion
Author Contributions
Funding
Conflicts of Interest
Data Availability
Ethical Statement
References
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References | States | Metrics |
---|---|---|
[40] | Lateral Index at Stress | |
[41] | Cognitive-Affective (Frontal Asymmetry) | |
[42] | Engagement | |
[43] | Alert/Stress | |
[44] | Valence | |
[44] | Arousal | |
[45] | Alzheimer | |
[46] | Event-related desynchronization | |
[47] | Neuronal activity | |
[48] | Load Index | |
[48] | Equanimity |
Dataset | Features | No. of Features |
---|---|---|
Subset_1 | Delta_AF4,Delta_T8,Delta_AF3,Delta_F3, Delta_F7, Delta_F8, Delta_FC5, Delta_O2, Delta_P8, Alpha_AF4, Alpha_F3, Alpha_F7, Alpha_F8, Alpha_FC5, Alpha_O2, Alpha_P8, Alpha_T8, Beta_AF3, Beta_AF4, Beta_F3, Beta_F7, Beta_F8, Beta_FC5, Beta_O2, Beta_P8, Beta_T8, Gamma_AF4, Gamma_F3, Gamma_F7, Gamma_F8, Gamma_FC5, Gamma_O2, Gamma_P8, Gamma_T8 | 36 |
Subset_2 | Alpha_AF4, Alpha_F3, Alpha_F7, Alpha_F8, Alpha_FC5, Alpha_O2, Alpha_P8, Alpha_T8 | 9 |
Subset_3 | Beta_AF4, Beta_F3, Beta_F7, Beta_F8, Beta_FC5, Beta_O2, Beta_P8, Beta_T8, Gamma_AF4, Gamma_F3, Gamma_F7, Gamma_F8, Gamma_FC5, Gamma_O2, Gamma_P8, Gamma_T8 | 18 |
Subset_4 | Alpha_AF4, Alpha_F3, Alpha_F7, Alpha_F8, Alpha_FC5, Alpha_O2, Alpha_P8, Alpha_T8, Beta_AF3, Beta_AF4, Beta_F3, Beta_F7, Beta_F8, Beta_FC5, Beta_O2, Beta_P8, Beta_T8, | 18 |
Subset_5 | Alpha_AF4, Alpha_F3, Alpha_F7, Alpha_F8, Alpha_FC5, Alpha_O2, Alpha_P8, Alpha_T8, Beta_AF3, Beta_AF4, Beta_F3, Beta_F7, Beta_F8, Beta_FC5, Beta_O2, Beta_P8, Beta_T8, Gamma_AF4, Gamma_F3, Gamma_F7, Gamma_F8, Gamma_FC5, Gamma_O2, Gamma_P8, Gamma_T8 | 27 |
Subset_6 | Delta_AF4, Delta_T8, Delta_AF3, Delta_F3, Delta_F7, Delta_F8, Delta_FC5, Delta_O2, Delta_P8, Alpha_AF4, Alpha_F3, Alpha_F7, Alpha_F8, Alpha_FC5, Alpha_O2, Alpha_P8, Alpha_T8, Beta_AF3, Beta_AF4, Beta_F3, Beta_F7, Beta_F8, Beta_FC5, Beta_O2, Beta_P8, Beta_T8 | 27 |
Subset_7 | Delta_AF4, Delta_T8, Delta_AF3, Delta_F3, Delta_F7, Delta_F8, Delta_FC5, Delta_O2, Delta_P8, Alpha_AF4, Alpha_F3, Alpha_F7, Alpha_F8, Alpha_FC5, Alpha_O2, Alpha_P8, Alpha_T8, Gamma_AF4, Gamma_F3, Gamma_F7, Gamma_F8, Gamma_FC5, Gamma_O2, Gamma_P8, Gamma_T8 | 27 |
ISA | NASA-TLX | ER | ||||
---|---|---|---|---|---|---|
Subjects | Task_1 | Task_2 | Task_1 | Task_2 | Task_1 | Task_2 |
Subject_1 | 16.66 | 34.44 | 4.33 | 65.67 | 3 | 12 |
Subject_3 | 31.10 | 57.77 | 12.67 | 56.67 | 4 | 7 |
Subject_4 | 25.55 | 51.10 | 20.33 | 70.67 | 3 | 8 |
Subject_5 | 21.10 | 43.33 | 64.33 | 68.67 | 2 | 4 |
Total | 23.10 | 43.32 | 28.33 | 61.80 | 19 | 34 |
Bands | Task | Mean | Std. Deviation |
---|---|---|---|
Delta | Task_1 | 10.9193 | 1.20741 |
Task_2 | 9.8171 | 0.5733 | |
Theta | Task_1 | 10.2063 | 0.4682 |
Task_2 | 9.9971 | 0.11242 | |
Alpha | Task_1 | 10.4613 | 0.48171 |
Task_2 | 10.6696 | 0.46037 | |
Beta | Task_1 | 22.4447 | 0.89813 |
Task_2 | 23.2951 | 0.3818 | |
Gamma | Task_1 | 15.5624 | 0.19241 |
Task_2 | 15.8033 | 0.16196 |
Task_1 | Task_2 | p-Value | |
---|---|---|---|
M ± SD | M ± SD | ||
NASA-TLX | 25.41 ± 715.7 | 65.42 ± 38.25 | p ≤ 0.048 |
ISA | 23.60 ± 38.18 | 46.66 ± 101.24 | p ≤ 0.001 |
ER | 3 ± 0.66 | 8.25 ± 8.25 | p ≤ 0.028 |
DELTA | 0.106 ± 0.084 | 0.028 ± 0.040 | p ≤ 0.038 |
THETA | 0.056 ± 0.032 | 0.041 ± 0.007 | p ≤ 0.383 |
ALPHA | 0.074 ± 0.033 | 0.088 ± 0.032 | p ≤ 0.05 |
BETA | 0.917 ± 0.063 | 0.977 ± 0.026 | p ≤ 0.036 |
GAMMA | 0.432 ± 0.013 | 0.449 ± 0.011 | p ≤ 0.005 |
Subjective | Performance | Physiological Measures | ||||||
---|---|---|---|---|---|---|---|---|
ISA | NASA | RT | Alpha | Beta | Delta | Gamma | Theta | |
ISA | --- | |||||||
NASA | 0.598 | --- | ||||||
RT | 0.612 | 0.538 | --- | |||||
Alpha | 0.301 | −0.168 | 0.680 | --- | ||||
Beta | 0.488 | −0.113 | 0.642 | 0.873 | --- | |||
Delta | −0.519 | −0.097 | −0.745 | −0.830 | −0.894 | --- | ||
Gamma | 0.610 | 0.062 | 0.815 | 0.851 | 0.856 | −0.805 | --- | |
Theta | −0.121 | 0.206 | −0.247 | −0.592 | −0.727 | 0.768 | −0.329 | --- |
Subset | Chromosomes | Features Selection | # Gens | Acc | ER | Time (s) |
---|---|---|---|---|---|---|
Subset 1 | [0,1,1,1,0,0,0,1,0,1,1,1,1,0,0,0,1,1,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0] | ‘Delta_AF4′, ‘Delta_F3′, ‘Delta_F7′, ‘Delta_P8′, ‘Alpha_AF3′, ‘Alpha_AF4′, ‘Alpha_F3′, ‘Alpha_F7′, ‘Alpha_P8′, ‘Alpha_T8′, ‘Beta_F7′, ‘Beta_P8′, ‘Gamma_F7′ | 13 | 97.7% | 2.26% | 580.84 |
Subset 2 | [0,0,1,0,1,0,1,0,0] | ‘Alpha_F3′, ‘Alpha_F8′, ‘Alpha_O2′ | 3 | 77.34% | 22.6% | 201.67 |
Subset 3 | [1,1,1,0,1,1,0,1,1,0,1,0,0,1,1,0,0,1] | ‘Beta_AF3′, ‘Beta_AF4′, ‘Beta_F3′, ‘Beta_F8′, ‘Beta_FC5′, ‘Beta_P8′, ‘Beta_T8′, ‘Gamma_AF4′, ‘Gamma_F8′, ‘Gamma_FC5′, ‘Gamma_T8′ | 11 | 88.7% | 11.2% | 394.05 |
Subset 4 | [1,1,0,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1] | ‘Alpha_AF3′, ‘Alpha_AF4′, ‘Alpha_F7′, ‘Alpha_F8′, ‘Alpha_FC5′, ‘Alpha_O2′, ‘Alpha_P8′, ‘Alpha_T8′, ‘Beta_AF3′, ‘Beta_AF4′, ‘Beta_F3′, ‘Beta_F7′, ‘Beta_FC5′, ‘Beta_O2′, ‘Beta_P8′, ‘Beta_T8′ | 16 | 94.4% | 5.55% | 455.52 |
Subset 5 | [0,1,1,1,1,1,1,1,1,0,0,0,1,0,1,1,0,1,1,0,1,0,1,0,1,0,1] | ‘Alpha_AF4′, ‘Alpha_F3′, ‘Alpha_F7′, ‘Alpha_F8′, ‘Alpha_FC5′, ‘Alpha_O2′, ‘Alpha_P8′, ‘Alpha_T8′, ‘Beta_F7′, ‘Beta_FC5′, ‘Beta_O2′, ‘Beta_T8′, ‘Gamma_AF3′, ‘Gamma_F3′, ‘Gamma_F8′, ‘Gamma_O2′, ‘Gamma_T8′ | 17 | 95.4% | 4.51% | 637.29 |
Subset 61 | [1,0,1,1,0,0,1,1,1,0,1,1,1,1,1,0,0,0,1,1,1,1,0,1,1,0,1] | ‘Delta_AF3′, ‘Delta_F3′, ‘Delta_F7′, ‘Delta_O2′, ‘Delta_P8′, ‘Delta_T8′, ‘Alpha_AF4′, ‘Alpha_F3′, ‘Alpha_F7′, ‘Alpha_F8′, ‘Alpha_FC5′, ‘Beta_AF3′, ‘Beta_AF4′, ‘Beta_F3′, ‘Beta_F7′, ‘Beta_FC5′, ‘Beta_O2′, ‘Beta_T8′ | 18 | 96.5% | 3.42% | 618.34 |
Subset 62 | [1,0,1,1,1,0,1,1,1,0,0,0,0,0,0,1,0,1,0,1,1,0,0,1,0,0,1] | ‘Delta_AF3′, ‘Delta_F3′, ‘Delta_F7′, ‘Delta_F8′, ‘Delta_O2′, ‘Delta_P8′, ‘Delta_T8′, ‘Alpha_O2′, ‘Alpha_T8′, ‘Beta_AF4′, ‘Beta_F3′, ‘Beta_FC5′, ‘Beta_T8′ | 13 | 96.5% | 3.42% | 618.34 |
Subset 63 | [1,0,0,1,1,0,1,1,0,0,0,0,0,0,1,0,0,0,1,1,0,0,0,1,1,0,0] | ‘Delta_AF3′, ‘Delta_F7′, ‘Delta_F8′, ‘Delta_O2′, ‘Delta_P8′, ‘Alpha_FC5′, ‘Beta_AF3′, ‘Beta_AF4′, ‘Beta_FC5′, ‘Beta_O2′ | 10 | 96.5% | 3.42% | 618.34 |
Subset 64 | [0,0,0,0,0,0,0,0,1,1,0,0,1,0,0,0,1,1,1,1,0,0,0,0,1,0,0,1] | ‘Delta_T8′, ‘Alpha_AF3′, ‘Alpha_F7′, ‘Alpha_P8′, ‘Alpha_T8′, ‘Beta_AF3′, ‘Beta_AF4′, ‘Beta_O2′ | 8 | 96.5% | 3.42% | 618.34 |
Subset 7 | [1,1,0,1,1,0,0,0,1,1,1,1,0,1,0,1,1,0,1,1,1,1,1,1,1,0,1] | ‘Delta_AF3′, ‘Delta_AF4′, ‘Delta_F7′, ‘Delta_F8′, ‘Delta_T8′, ‘Alpha_AF3′, ‘Alpha_AF4′, ‘Alpha_F3′, ‘Alpha_F8′, ‘Alpha_O2′, ‘Alpha_P8′, ‘Gamma_AF3′, ‘Gamma_AF4′, ‘Gamma_F3′, ‘Gamma_F7′, ‘Gamma_F8′, ‘Gamma_FC5′, ‘Gamma_O2′, ‘Gamma_T8′ | 19 | 90.25% | 9.75% | 425.94 |
Subset | SVMRBF | k-NN | SVMLINEAL | LiR | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Train | Test | Sens | Train | Test | Sens | Train | Test | Sens | Train | Test | Sens | |
Subset 1 | 96.77 | 96.71 | 96.64 | 97.67 | 97.50 | 97.50 | 89.38 | 89.29 | 89.36 | 89.57 | 89.43 | 89.46 |
Subset 2 | 85.50 | 84.36 | 84.34 | 82.59 | 81.66 | 81.89 | 66.03 | 65.97 | 65.92 | 65.02 | 64.96 | 64.94 |
Subset 3 | 97.61 | 97.02 | 97.00 | 94.91 | 94.26 | 94.38 | 85.60 | 85.57 | 85.53 | 85.02 | 84.87 | 84.92 |
Subset 4 | 98.27 | 98.16 | 98.08 | 98.70 | 98.50 | 98.50 | 91.02 | 90.73 | 90.68 | 90.25 | 90.09 | 90.06 |
Subset 5 | 97.70 | 97.27 | 97.28 | 97.61 | 97.46 | 97.42 | 89.66 | 89.50 | 89.40 | 89.06 | 88.91 | 88.89 |
Subset 61 | 98.38 | 98.24 | 98.28 | 98.76 | 98.64 | 98.60 | 91.39 | 91.27 | 91.18 | 90.79 | 90.59 | 90.78 |
Subset 62 | 96.75 | 96.54 | 96.57 | 98.40 | 98.17 | 98.20 | 86.90 | 86.86 | 86.80 | 86.52 | 86.47 | 86.38 |
Subset 63 | 98.54 | 98.27 | 98.27 | 97.28 | 96.90 | 96.98 | 84.71 | 84.64 | 84.49 | 84.58 | 84.45 | 84.43 |
Subset 64 | 97.97 | 97.72 | 97.67 | 95.38 | 95.03 | 94.84 | 79.97 | 79.96 | 79.90 | 79.59 | 79.50 | 79.51 |
Subset 7 | 97.55 | 97.17 | 97.14 | 96.73 | 96.50 | 96.35 | 85.08 | 84.94 | 84.78 | 92.95 | 92.82 | 92.80 |
Total | 96.50 | 96.14 | 96.64 | 95.80 | 95.46 | 95.47 | 84.97 | 84.87 | 84.80 | 85.33 | 85.21 | 85.21 |
Subset | GALoRIS | MI | PCA | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
SVM RBF | k-NN | SVM | LiR | SVM RBF | k-NN | SVM | LiR | SVM RBF | k-NN | SVM | LiR | |
Subset 1 | 96.77 | 97.50 | 89.29 | 89.43 | 87.78 | 86.87 | 76.37 | 77.40 | 80.48 | 80.08 | 69.03 | 68.78 |
Subset 2 | 84.36 | 81.66 | 65.97 | 64.96 | 98.78 | 98.17 | 98.32 | 97.65 | 98.66 | 99.33 | 98.62 | 98.72 |
Subset 3 | 97.02 | 94.26 | 85.57 | 84.87 | 88.00 | 86.87 | 76.37 | 77.40 | 86.05 | 85.38 | 83.46 | 83.43 |
Subset 4 | 98.16 | 98.50 | 90.73 | 90.09 | 84.65 | 81.21 | 78.47 | 76.85 | 79.38 | 78.19 | 60.44 | 61.26 |
Subset 5 | 97.70 | 97.46 | 89.50 | 88.91 | 87.78 | 86.87 | 76.37 | 77.40 | 76.33 | 75.06 | 62.39 | 62.08 |
Subset 6 | 97.91 | 97.18 | 85.68 | 85.25 | 87.08 | 85.26 | 78.68 | 77.43 | 83.16 | 82.42 | 68.17 | 67.75 |
Subset 7 | 97.17 | 96.50 | 84.94 | 92.82 | 85.53 | 82.06 | 76.40 | 76.12 | 79.59 | 79.26 | 65.89 | 65.46 |
Total | 96.14 | 95.46 | 84.87 | 85.21 | 88.51 | 86.76 | 80.14 | 80.04 | 83.38 | 82.82 | 72.57 | 72.50 |
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Becerra-Sánchez, P.; Reyes-Munoz, A.; Guerrero-Ibañez, A. Feature Selection Model based on EEG Signals for Assessing the Cognitive Workload in Drivers. Sensors 2020, 20, 5881. https://doi.org/10.3390/s20205881
Becerra-Sánchez P, Reyes-Munoz A, Guerrero-Ibañez A. Feature Selection Model based on EEG Signals for Assessing the Cognitive Workload in Drivers. Sensors. 2020; 20(20):5881. https://doi.org/10.3390/s20205881
Chicago/Turabian StyleBecerra-Sánchez, Patricia, Angelica Reyes-Munoz, and Antonio Guerrero-Ibañez. 2020. "Feature Selection Model based on EEG Signals for Assessing the Cognitive Workload in Drivers" Sensors 20, no. 20: 5881. https://doi.org/10.3390/s20205881