Abstract:
A Monitoring and predicting human cognitive state and performance using physiological signals such as Electroencephalogram (EEG) have recently gained increasing attention in the fields of brain-computer interface.
Development of the drowsiness monitoring technology for preventing accidents behind the steering wheel has become a major area of research interest in the field of safety driving since drivers' fatigue is a primary causal factor in many accidents. Monitoring of drowsiness in derivers is very important because of the marked decline in the drivers' abilities of perception, recognition, and vehicle control while feeling sleepy. It is known that physiological changes such as eye activities, heart rate variability (HRV), and particularly, the electroencephalogram (EEG) activities co-vary with drowsiness levels.
Our main task is to acquire the EEG signals, and after processing those signals we will differentiate between the two states of mind that are..
Stage W : Wakefulness
Stage N1 : Transition from wakefulness into light sleep
Brain waves slow to four to seven cycles per second
Spend about five minutes in Stage N1 (Theta State)
So by differentiating between these two we can determine if the driver is alert or not and in the case he is losing his alertness level, our system will alarm the driver.
In the start we’ll use different data sets and will develop our algorithm to test those data sets, then we’ll take our own recordings and same algorithm will be tested on these recordings to confirm the real time testing of the algorithm.
