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To meet the safety requirements for driver fatigue detection and warning in the most common L2 level intelligent driving scenarios, a four-class classification of driver states is achieved using EEG signals collected by a multi-channel wireless EEG cap. A convolutional recurrent neural network is used to train models using different combinations of frequency-domain, time-domain and nonlinear features. The results show that the best recognition performance is achieved when combining the differential entropy from nonlinear features with the average absolute value from time-domain features. Furthermore, three integration strategies are proposed to fuse base classifiers trained on different feature combinations. The method achieves accurate multi-class classification of driver fatigue states in a cost-effective and user-friendly manner, and promotes the application of wearable devices in driving scenarios to improve driving safety.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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