A Context-Aware EEG Headset System for Early Detection of Driver Drowsiness
<p>(<b>a</b>) Block diagram of the proposed system; (<b>b</b>) The top view of the designed headset; (<b>c</b>) The full view of the headset; (<b>d</b>) The fabricating procedure using 3D printer; (<b>e</b>) The fabricated headset prototype.</p> "> Figure 2
<p>Structure of EEG bio-potentials conditioning circuit and a pair of commercial dry electrodes used. The locations marked by red dotted circles are the locations EEG electrodes attached in this study.</p> "> Figure 3
<p>The classification model building chain containing three different tasks: data collection, classification validation and classification optimization.</p> "> Figure 4
<p>The procedure of LOO cross-validation and optimization, where S<sub>i</sub> means the <span class="html-italic">i</span>-th subject and Accuracy_i means the classification accuracy for <span class="html-italic">i</span>-th Round.</p> "> Figure 5
<p>The working flowchart of smartphone which consists of two <span class="html-italic">Activities</span> (User interfaces) and <span class="html-italic">Services</span> (The functions running in background).</p> "> Figure 6
<p>Comparison of EEG signals (line chart) and band power (pie chart) between wet electrodes (the top) and dry electrodes (the bottom). For the line chart, <span class="html-italic">X</span>-axis indicates the time (second). <span class="html-italic">Y</span>-axis indicates the amplitude of the digitalized EEG samples which are already filtered by the digital band-pass filter (4–30 Hz) in SPU. For the pie chart, the <span class="html-italic">X</span>-axis indicates the frequency ranged from 0–64 Hz (half of the sampling rate 128 Hz). <span class="html-italic">Y</span>-axis indicates the magnitude of FFT power.</p> "> Figure 7
<p>Box-Whiskers plots of (<b>a</b>) EEG and (<b>b</b>) gyroscope features. The boxes have three lines to present the values for first quartile (the bottom), median, and third quartile (the top) for column data. The length between the first quartile (Q1) and the third quartile (Q3) is called interquartile range (IQR). Two addition lines at both ends of the whisker indicate the Q1 − 1.5 × IQR and Q3 + 1.5 × IQR value of a column data. Any data not included between the whiskers is plotted as outliers represented by “o” for mild outliers and “*” for extreme outliers. The number next to the outlier is the number of the data in that column, called case number; (<b>c</b>) ROC curve showing sensitivity (possibility of true drowsy event) and 1-specificity (possibility of false drowsy event) for extracted EEG and gyroscope features.</p> "> Figure 8
<p>The typical slightly drowsy symptom, yawning, captured by video as well as EEG and gyroscope from a representative subject. The blue line charts represent EEG raw signals, <span class="html-italic">X</span>-axis, <span class="html-italic">Y</span>-axis and <span class="html-italic">Z</span>-axis signal of the gyroscope, respectively. The two bars on the right side of the line charts represent the EEG RBP features (the top) and the gyroscope MP features (the bottom).</p> "> Figure 9
<p>Screenshot of the Android smartphone application that shows the EEG and gyroscope features, the estimation of the driving status, and the raw data of EEG and 3-axis gyroscope.</p> ">
Abstract
:1. Introduction
2. System Design
2.1. Wireless Context-Aware EEG Headset
2.1.1. SIU
2.1.2. SPU
2.1.3. Signal Analysis and Feature Extraction
2.2. Classifier
2.2.1. Theoretical Principle of SVM Classifier
- 1)
- Linear kernel
- 2)
- RBF kernel
2.2.2. LOO Cross-Validation and Optimization
2.3. Smartphone
3. System Evaluation Design and Materials
4. System Evaluation Results
4.1. EEG Signal Quality Test
4.2. Feature Analysis
4.3. Detection Accuracy
Kernel | EEG Features (RBP (θ), RBP (α), RBP (β)) | Gyroscope Feature MP | Hybrid Features (RBP (θ), RBP (α), RBP (β), MP) | ||||||
Sens | Spec | Acc | Sens | Spec | Acc | Sens | Spec | Acc | |
Linear | 100 | 0 | 74.43 | 96.46 | 63.24 | 87.96 | 96.46 | 95.59 | 96.24 |
C = 0.01 | C = 0.01 | C = 2 | |||||||
RBF | 95.45 | 45.59 | 82.71 | 93.43 | 91.18 | 92.86 | 96.46 | 91.18 | 95.11 |
C = 2 | C = 1 | C = 5 | |||||||
g = 0.1 | g = 0.01 | g = 0.01 |
4.4. Real-Time Performance
Condition | Feature Extraction Approach | Power Consumption (mA) | Battery Life (h) | |
---|---|---|---|---|
Power supply 3.6 V | BLE | Remote | 63 | 41 |
Battery capacity: 2600 mA·h | On chip | 56 | 46 | |
Sampling rate: 128 Hz | Bluetooth v2.0 EDR+ | Remote | 82 | 32 |
ADC resolution: 12 bits | On chip | 75 | 35 | |
Bluetooth (slave) : Active | ||||
Baud-rate: 115,200 bps |
5. Discussion
5.1. Principle Results
5.2. Comparison with Prior Work
5.2.1. EEG versus Other Physiological Signals
5.2.2. Signal Processing Comparison
5.2.3. Detection Accuracy Comparison
5.3. Limitation
6. Conclusions and Future Work
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Li, G.; Chung, W.-Y. A Context-Aware EEG Headset System for Early Detection of Driver Drowsiness. Sensors 2015, 15, 20873-20893. https://doi.org/10.3390/s150820873
Li G, Chung W-Y. A Context-Aware EEG Headset System for Early Detection of Driver Drowsiness. Sensors. 2015; 15(8):20873-20893. https://doi.org/10.3390/s150820873
Chicago/Turabian StyleLi, Gang, and Wan-Young Chung. 2015. "A Context-Aware EEG Headset System for Early Detection of Driver Drowsiness" Sensors 15, no. 8: 20873-20893. https://doi.org/10.3390/s150820873