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Drowsiness has become one of the major causes of road accidents now-a-days. In order to alleviate this issue, a system has been developed, which uses electroencephalogram (EEG) signals to detect drowsiness with sufficient reliability. This experiment was conducted on a small population and the EEG signals were acquired using a 14-channel wireless headset, while they were in a virtual driving environment. To extract the eye closures, the EEG signal was segmented, and pre-processed. Further the scalograms which describes the time-frequency characteristics of these segments were taken. Pretrained Convolutional Neural Network based architectures viz. ResNet-152, ResNet101, VGG16, VGG19, AlexNet were used to distinguish three states of the driver namely “Sleepy or Drowsy”, “Asleep” and “Awake”.

Orthogonal Frequency Division Multiplexing (OFDM) is one of the latest wireless communication technologies, which aids transmission with the high data rate, reliability, and spectral efficiency. Our study involved simulation of an OFDM system using 32-QAM symbol mapping, over a Rician channel model. A comparative analysis involving three different detection techniques: maximum likelihood, minimum mean square error, and zero-forcing in terms of MSE and BER are carried out. The analysis shows that amongst the detection techniques, Minimum Mean Square Error yields the lowest values of MSE and BER, for SNR ranging from 0 to 30 dB.