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Data Classification Through Cognitive Computing

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Computing for Data Analysis: Theory and Practices

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Abstract

A brain–computer interface (BCI) is a direct link between the human brain and an external system. Computer–brain interfaces are used to send sensory information from the brain or to activate the brain with electrical signals created artificially. BCI offers a wide range of applications, including wheelchair control, spelling aids, robot control, and cursor control, among others. Cursor control is one of the most significant applications for impaired individuals to communicate successfully with the application interface. Cursor movement is classified as either single target or multitarget. Electroencephalography (EEG), functional magneto resonance imaging (fMRI), near-infrared spectroscopy (NIRS), and magneto encephalography are all methods for capturing brain signals (MEG). EEG is the most commonly employed among them for signal collection. Based on frequency, EEG divides the brain rhythm into five categories (α, β, θ, δ, γ). People can efficiently operate the cursor based on different types of brain rhythms by converting the scale voltage of the brain signal into amplitude level as a cursor. However, because disabled people or people with neuromuscular disorders are unable to use their left or right limbs, they require a BCI tool that can operate the cursor. If impaired people can properly move the cursor, they can reply to the outside world relatively fast. For multitarget-based BCI, disabled persons can use a machine learning technique to select a series of nearby targets and then control the cursor movement to each target one by one using an efficient method. As paralyzed persons are unable to convey their emotions owing to a significant problem, emotion detection for them is a promising potential in cognitive research. Emotion may be identified from the brain signal because it is collected in several electrodes, each of which is linked to underlying lobes in the brain, such as electrode F3, P3, which is put over the frontal and parietal lobes, respectively. However, the activation or deactivation of brain signals in each lobe of the brain is linked to a certain emotion, which can be classified using a sophisticated machine learning algorithm. Disabled people can interact effectively with the outside world if their emotions are properly categorized.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Techno International New Town, Kolkata, West Bengal, India

    Sanjay Chakraborty

  2. Department of Computer Science and Engineering, Heritage Institute of Technology, Kolkata, West Bengal, India

    Lopamudra Dey

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  1. Sanjay Chakraborty
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Chakraborty, S., Dey, L. (2023). Data Classification Through Cognitive Computing. In: Computing for Data Analysis: Theory and Practices. Data-Intensive Research. Springer, Singapore. https://doi.org/10.1007/978-981-19-8004-6_6

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