Real time fatigue-driver detection from electroencephalography using Emotiv EPOC+

The fatigue driving detection has been developed with many kinds of approaches, such as video using face expressions and Electroencephalography (EEG) that uses the brainwave signals of the driver. This paper proposes a method to implement the driving fatigue detection in real time using Python and Emotiv EPOC+ with 14 channels. The EEG recorded database will extract their features per-30 seconds. The prediction process gets the EEG recorded data from the driver doing the driving simulation and trains it using the extracted features data from the database. The results print as Fit and Alert, or Fatigue and Sleepy. The contributions of the authors in this paper are as follows: i) the reduction of the processing time, such as reading input and output files and communicating among different programming languages; ii) the analysis and comparison of the dynamics of prediction results and significant channels from the results of the previous research, and iii) the development of the system from semi real-time to real-time forecasting.