Brain Computer Interface Systems (BCIs) allow the identification of volitive brain activity patterns. This allows their use as input channels for alternative communication and computer access systems by patients suffering from severe motor disabilities. This paper presents preliminary results obtained after extracting four different features from EEG signals in order to recognize the activity patterns by means of four different classifiers. The final goal is to determine the proper "feature -classifier" binomial for each user in order to increase system reliability and satisfaction in use.
The study of fatigue has increased by leaps and bounds; however, still no universal definition exists and fatigue remains a vague concept used as an umbrella term that comprises of physiological, emotional, and behavioral factors that can result in chronic physical or mental states affecting the cognitive, memory, motoric, and perceptive capacities. Biomedical engineering has faced fatigue evaluation from two different approaches: exercise‐induced fatigue (or metabolic fatigue) and central fatigue (related to the evolution of the subject condition caused by the execution of normal tasks). As metabolic fatigue is a complex mechanism usually discussed by exercise physiology, this work will center on central fatigue detection and evaluation, because of its importance within the field of human factors engineering in occupational health studies. Central fatigue has been referred to in various terms that are more or less mutually interchangeable in the research literature: loss of alertness or vigilance, fatigue, and drowsiness, although no significant difference exists among them.
Research has focused on the development of objective and reliable measurement techniques able to quantify the state of human deteriorated condition, although none of the proposed methods has been yet considered to be a gold standard. This work reviews several fatigue indicators that have been reported to reflect the different dimensions resulting from this state [heart‐related measures such as the heart rate (HR) and heart rate variability (HRV), muscles activity through electromyogram (EMG) and mechanomyogram (MMG), spectral analysis of the electroencephalograph (EEG) and event‐related potential (ERP), ocular metrics, speech parameters, etc].
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