Generally, there are devices in the health care, scientific research and mental health fields related to general pattern recognition of brain signals and to the recognition of brain activity. For example, there are techniques and apparatuses that measure brain activity through invasive examination, such as deep computing techniques to organize and respond to data from implantable medical devices and invasive examination using implantable electrodes. There are also techniques and apparatuses that measure brain activity through non-invasive examination. For example, there are methods and apparatuses for examining brain function through detection of the size of a subject's pupil, as well as through detection of electrical signals via an electroencephalogram (EEG), an electomyogram (EMG) and/or through electro-oculograph (EOG) techniques. As another example, there are other methods and apparatuses for recording electromagnetic activity in the brain, selecting a target pattern of electromagnetic activity produced by the brain and analyzing the sample of electromagnetic activity of the brain to identify a portion which contains the target pattern. However, such apparatuses and techniques lack complex signal analysis necessary to extract data from multiple sources.
Accordingly, there is a need for an apparatus and for a method for monitoring one or more physiological variables associated with cognitive/affective brain states via multiple sensors, filtering the data to reduce the complexity of the data before applying the learning algorithms that correlate the data with examined brain state, and applying pattern recognition techniques and technology in recognizing a brain state of a subject.