• Phalguna Peravali
• Navya.Y
• Nagendra.N
• Rajeshkumar.R
• Dr. B.Shanmugham

EEG (Electroencephalogram) signal is considered as one of the most complicated signal having low amplitude which makes its analysis very difficult. The EEG signal properties can be magnified by using the wavelets which helps in performing much closer analysis of the signal. The various brain waves like alpha, beta, theta, gamma and delta can be studied and related with the abnormalities which further relate analysis of workload detection. Recently various research approaches have been progressed and proposed for analyzing EEG signal. According to these proposed papers EEG signals were recorded from the scalp of the brain and are measured in response to various workloads. The EEG signal features are extracted and the analysis of the system has to be done to separate the pattern of the signal and correlate with the predefined features. For this reason, different Quantization methods are selected and then the particular wave pattern is identified by comparing with Memory based tasks like a Cognitive task, mathematical tasks, past memory and memory remembrance in EEG which are extracted from a normal subject. After feature extraction, the EEG signals are delivered in to the processor and processed using BIOPAC data acquisition systems and MATLAB. MATLAB provides a cooperative graphical user interface (GUI), which allows users to openly an interactively analyze their high-density EEG dataset and then additional signal information using dissimilar methods like ICA and time/frequency analysis (TFA). In addition to fixed averaging methods, the research work resolves dissimilar brain signals through associating, analyzing and simulating datasets which is before restrained in the MATLAB software to practice EEG signals. The results have shown the potential of EEG signal to envision the different levels of workload through final validation of the brain signal.

WORK LOAD ANALYSIS AND CLASSIFICATION OF EEG USING ADVANCED DATA ACQUISITION SYSTEMS

"WORK LOAD ANALYSIS AND CLASSIFICATION OF EEG USING ADVANCED DATA ACQUISITION SYSTEMS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 3, page no.238-243, March-2019, Available :http://www.jetir.org/papers/JETIRAK06044.pdf

"WORK LOAD ANALYSIS AND CLASSIFICATION OF EEG USING ADVANCED DATA ACQUISITION SYSTEMS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 3, page no. pp238-243, March-2019, Available at : http://www.jetir.org/papers/JETIRAK06044.pdf