Technically, a feature represents a distinguishing property, a recognizable measurement, and a functional component obtained from a section of a pattern. Extracted features are meant to minimize the loss of important information embedded in the signal. In addition, they also simplify the amount of resources needed to describe a huge set of data accurately. This is necessary to minimize the complexity of implementation, to reduce the cost of information processing, and to cancel the potential need to compress the information. More recently, a variety of methods have been widely used to extract the features from EEG signals, among these methods are time frequency distributions (TFD), fast fourier transform (FFT), eigenvector methods (EM), wavelet transform (WT), and auto regressive method (ARM), and so on. In general, the analysis of EEG signal has been the subject of several studies, because of its ability to yield an objective mode of recording brain stimulation which is widely used in brain-computer interface researches with application in medical diagnosis and rehabilitation engineering. The purposes of this paper, therefore, shall be discussing some conventional methods of EEG feature extraction methods, comparing their performances for specific task, and finally, recommending the most suitable method for feature extraction based on performance.

This paper highlights a new detection method based on higher spectral analysis techniques to distinguish the Electrocardiogram (ECG) of normal healthy subjects from that with a cardiac ischaemia (CI) patient. Higher spectral analysis techniques provide in-depth information other than available conventional spectral analysis techniques usually used with ECG analysis. They provide information within frequency parts and information regarding phase associations. Bispectral analysis- Bispectrum and Quadratic Phase Coupling techniques are utilized to detect as well as to characterize phase combined harmonics in ECG. The work is developed, tested and validated using Normal Sinus Rhythm Data from the MIT-BIH Database and CI data from the ST Petersburg European ST-T Database. The results validate the efficacy of the introduced method by maintaining 100% sensitivity and achieving 93.33% positive predictive accuracy. The simplicity and robustness of the proposed method makes it feasible to be used within available ECG systems.

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