Classification in EEG-Based Brain Computer Interfaces Using Inverse Model

Emotions are an unstoppable and uncontrollable aspect of human reaction to an action. It is a proved mechanism that encompasses variation in emotions due to different acts, expectations, focus to goals, appraisal and resulting reactions. When human body experiences different emotions like fear, anger, sad, happiness then there is also change in different bio signals which are flowing in human body. These biosignals are: Galvanic skin conductance (GSR), blood volume pulse (BVP), brain waves (EEG), muscle tension, temperature, and respiration. The overall objective of this work is to design and develop a low power, portable, and cost effective embedded system through which we can measure different parameters of autonomic nervous system (GSRI temperature/Heart rate/EEG) of a person and display it on any of the output devices by using effective algorithm for stress recognition using learning systems. This portable embedded system will be using MSP430F2013 microcontroller from Te xas Instruments. This system will help measure different parameters such as GSR and BVP. This paper is also discusses an experiment which identifies the stimuli triggering off the emotional state of the person as a result of variations in the bio signals.

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Self-supervised Contrastive Few-Shot Learning for Motor Imagery Brain-Computer Interfaces

Zhang,

Jiang,

et al. 2023

2023 12th International Conference on Computing and Pattern Recognition

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Classification in EEG-Based Brain Computer Interfaces Using Inverse Model

Cited by 7 publications

References 5 publications

Analysis of EEG signal to investigate the influence of intoxication in perception delay

Analysis of EEG signal to investigate the influence of intoxication in perception delay

Intelligent data analysis algorithms on biofeedback signals for estimating emotions

Self-supervised Contrastive Few-Shot Learning for Motor Imagery Brain-Computer Interfaces

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