Modeling of cognition using EEG: A review and a new approach

Abstract

Understanding the secrets underlying the brain functioning would be the noble achievement of this era. Learning how brain learns would be the milestone to guide the researchers of artificial intelligence, neurology and psychology. With the advent of "Integrate and Fire" model of neuron proposed about a hundred years ago, the brain research has picked up its pace in the study of different aspects of brain functionality. Many cogni tive architectures have been proposed with an aim of simulating and understanding human cognition. On the other hand, many technologies have emerged that can measure the parameters of the brain activity. Among them, Electroencephalogram (EEG) stands as a reliable tool in the study of brain functioning.Simplified wireless EEGs are readily available now which can send data recorded by its electrodes to a computer for further processing. We have chosen this tool to detect different aspects of cognition and to predict the brain functioning behind it. A lot of studies from the past two decades have already revealed varying EEG patterns related to cognition. In this paper, we have proposed to extract different features from visual, tactile, auditory and psychomotor stimuli to work on different cognitive aspects such as memory, emotion, arousal, fatigue and distraction and to investigate its affect on the EEG. A methodology to model cognitive functions by relating the varying event related potential, brain waves, spectral density and latency in EEG outcomes are then related with the stimuli features to predict the cognitive state of mind.

Brain computer interfacing: Applications and challenges

“…It has been found that distraction and fatigue are two main sources for driver's inattention, which is considered as a strong cause for most traffic accidents [63]. Various types of measures have contributed in determining the driver's cognitive state [64][65][66]. Uses of EEG signals for fatigue detection have been widely studied in [67], while [68] has discussed the utilization of workload index to assess the driver's mental state.…”

Study on the Psychological States of Olfactory Stimuli Using Electroencephalography and Heart Rate Variability

“…EEG can also be used to investigate the effects of various stimuli on the brain, including sensory input like smell, sound, and light, or cognitive tasks like problem-solving or decision-making [14,15]. In addition, it has been used to recognize emotions and represent cognitive functions such as distraction, fatigue, and concentration [16][17][18][19]. Alarcao and Fonseca reported an emotion estimation survey using EEG signals to detect unconscious emotions from EEG signals [20].…”

Mind-Machine Interface: Its Implementation and Applications in Real Field

“…They can analyze the impact of workload mental fatigue as well as the task time on EEG features 38 . Different measures have helped for finding the driver's cognitive state [39][40][41] . Alcoholic drivers could be characterized using EEG signals of brain [42][43][44] .…”