Direct Assessment of Alcohol Consumption in Mental State Using Brain Computer Interfaces and Grammatical Evolution
Abstract
:1. Introduction
2. Related Work
3. Materials and Methods
3.1. Brain Computer Interface Devices and Software
3.2. Feature Extraction
3.3. Classification Using Grammatical Evolution
- The next element from the vector is taken (denoted as V).
- The production rule is selected using the scheme Rule = V mod R, where R is the number of production rules for the current non-terminal symbol.
Algorithm 1 |
1. Initialization step
(a) For i = 1 to NG do
3. Evaluation step
|
4. Experimental Results
4.1. Dataset
4.2. Experimental Procedure
4.3. Classification Results
5. Discussion
6. Conclusions
Author Contributions
Conflicts of Interest
References
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Features | Feature Details |
---|---|
Time-based features | Mean value |
Variance | |
Range (max–min) | |
Median value | |
Inter-Quantile Range | |
Percentiles | |
Spectral features | Spectrum average in gamma band (25–40 Hz) |
Spectrum average in beta band (12–25 Hz) | |
Spectrum average in alpha band (8–12 Hz) | |
Spectrum average in theta band (4–8 Hz) | |
Spectrum average in delta band (1–4 Hz) |
Feature Selection | Classifier | Scenario 1 | |
---|---|---|---|
Moderate Drinkers | Heavy Drinkers | ||
Principal Component Analysis | Decision Tree | 54.39%, (t = 29.285) * | 63.33%, (t = 24.141) * |
Linear Discriminant Analysis | 55.58%, (t = 40,239) * | 65.19%, (t = 20.725) * | |
MultiLayer Perceptron | 66.53%, (t = 14.567) * | 80.06%, (t = 8.398) * | |
k-Nearest Neighbor | 66.89%, (t = 18.075) * | 73.97%, (t = 14.633) * | |
Information Gain | Decision Tree | 58.99%, (t = 28.769) * | 59.51%, (t = 20.057) * |
Linear Discriminant Analysis | 53.15%, (t = 35.698) * | 50.27%, (t = 22.019) * | |
MultiLayer Perceptron | 55.43%, (t = 23.535) * | 58.89%, (t = 15.690) * | |
k-Nearest Neighbor | 62.92%, (t = 26.112) * | 60.33%, (t = 32.457) * | |
Correlation Attribute Evaluation | Decision Tree | 59.71%, (t = 28.387) * | 72.57%, (t = 17.691) * |
Linear Discriminant Analysis | 50.31%, (t = 21.372) * | 59.41%, (t = 18.706) * | |
MultiLayer Perceptron | 56.77%, (t = 31.751) * | 69.79%, (t = 10.324) * | |
k-Nearest Neighbor | 63.22%, (t = 34.711) * | 76.60%, (t = 8.508) * | |
Entropy-based | Decision Tree | 58.42%, (t = 31.053) * | 70.15%, (t = 20.633) * |
Linear Discriminant Analysis | 49.54%, (t = 22.118) * | 58.21%, (t = 26.308) * | |
MultiLayer Perceptron | 60.07%, (t = 30.640) * | 71.49%, (t = 11.294) * | |
k-Nearest Neighbor | 62.25%, (t = 19.294) * | 73.35%, (t = 15.475) * | |
Grammatical Evolution | 85.55 | 87.53 |
Feature Selection | Classifier | Scenario 2 | |
---|---|---|---|
Moderate Drinkers | Heavy Drinkers | ||
Principal Component Analysis | Decision Tree | 54.96%, (t = 36.905) * | 60.90%, (t = 23.464) * |
Linear Discriminant Analysis | 55.07%, (t = 20.997) * | 67.98%, (t = 13.365) * | |
MultiLayer Perceptron | 68.28%, (t = 14.641) * | 80.89%, (t = 8.469) * | |
k-Nearest Neighbor | 76.35%, (t = 3.451) *** | 84.04%, (t = 4.335) ** | |
Information Gain | Decision Tree | 50.31%, (t = 20.493) * | 58.73%, (t = 17.948) * |
Linear Discriminant Analysis | 44.68%, (t = 26.378) * | 51.92%, (t = 18.690) * | |
MultiLayer Perceptron | 48.91%, (t = 21.261) * | 61.00%, (t = 22.797) * | |
k-Nearest Neighbor | 57.08%, (t = 22.712) * | 62.91%, (t = 20.325) * | |
Correlation Attribute Evaluation | Decision Tree | 52.07%, (t = 18.237) * | 75.83%, (t = 8.038) * |
Linear Discriminant Analysis | 40.24%, (t = 27.991) * | 59.35%, (t = 25.952) * | |
MultiLayer Perceptron | 48.35%, (t = 25.775) * | 68.24%, (t = 18.666) * | |
k-Nearest Neighbor | 56.30%, (t = 16.395) * | 75.57%, (t = 11.341) * | |
Entropy-based | Decision Tree | 50.98%, (t = 18.085) * | 51.91%, (t = 31.298) * |
Linear Discriminant Analysis | 49.28%, (t = 29.809) * | 52.33%, (t = 28.626) * | |
MultiLayer Perceptron | 53.98%, (t = 23.165) * | 49.12%, (t = 22.302) * | |
k-Nearest Neighbor | 53.61%, (t = 14.929) * | 50.01%, (t = 31.960) * | |
Grammatical Evolution | 80.52% | 88.70% |
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Tzimourta, K.D.; Tsoulos, I.; Bilero, T.; Tzallas, A.T.; Tsipouras, M.G.; Giannakeas, N. Direct Assessment of Alcohol Consumption in Mental State Using Brain Computer Interfaces and Grammatical Evolution. Inventions 2018, 3, 51. https://doi.org/10.3390/inventions3030051
Tzimourta KD, Tsoulos I, Bilero T, Tzallas AT, Tsipouras MG, Giannakeas N. Direct Assessment of Alcohol Consumption in Mental State Using Brain Computer Interfaces and Grammatical Evolution. Inventions. 2018; 3(3):51. https://doi.org/10.3390/inventions3030051
Chicago/Turabian StyleTzimourta, Katerina D., Ioannis Tsoulos, Thanasis Bilero, Alexandros T. Tzallas, Markos G. Tsipouras, and Nikolaos Giannakeas. 2018. "Direct Assessment of Alcohol Consumption in Mental State Using Brain Computer Interfaces and Grammatical Evolution" Inventions 3, no. 3: 51. https://doi.org/10.3390/inventions3030051
APA StyleTzimourta, K. D., Tsoulos, I., Bilero, T., Tzallas, A. T., Tsipouras, M. G., & Giannakeas, N. (2018). Direct Assessment of Alcohol Consumption in Mental State Using Brain Computer Interfaces and Grammatical Evolution. Inventions, 3(3), 51. https://doi.org/10.3390/inventions3030051