Multispectrum Approach in Quantitative EEG: Accuracy and Physical Effort

The detection of neurophysiological features by means of electroencephalogram (EEG) is one of the most recurrent medical exams to be performed on human beings. As it stands, EEG trials are not always sufficient to deliver a clear and precise diagnosis for much pathology. Hence, it must be integrated with other exams. However, we can use all additional instrumental exams to improve the quality of the diagnosis because there are other constraints, namely, financial, medical, and individual. This paper presents an original implementation of EEG signal processing using filter diagonalization method to build a bispectrum and contour representation to discover possible abnormalities hidden in the signal for aided-diagnosis. Two different EEG signals are used for this scope. EEG signals are acquired simultaneously with electrocardiograms (ECG) and ergospirometric ones. ECG signals are also processed along with EEGs. A comparison is made with high order spectra approach. All experimental data regarding EEG, ECG, and ergospirometry are acquired during suspected-patient walking along a path of ~32 m for verifying the impact of fatigue on neurophysiological processes and vice versa.