Brain and artificial intelligence: from the viewpoint of spontaneous and task-evoked brain dynamics
Recently, the field of brain science often yields ‘big’ data and utilizes machine learning, which is central for the present artificial intelligence (AI) field and starts usually from extracting the hidden features. However, the data recorded from the brain are dynamic where the property of the da...
| Main Authors: | , |
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| Format: | Article |
| Language: | English English |
| Published: |
American Scientific Publishers
2019
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/74311/ http://irep.iium.edu.my/74311/ http://irep.iium.edu.my/74311/ http://irep.iium.edu.my/74311/1/74311_Brain%20and%20Artificial%20Intelligence_article.pdf http://irep.iium.edu.my/74311/2/74311_Brain%20and%20Artificial%20Intelligence_scopus.pdf |
| Summary: | Recently, the field of brain science often yields ‘big’ data and utilizes machine learning, which is
central for the present artificial intelligence (AI) field and starts usually from extracting the hidden
features. However, the data recorded from the brain are dynamic where the property of the data
changes with time, different from photos that are static over the time. Then, the following question
emerges: Are brain’s dynamic data really suitable for the present AI techniques? More specifically,
can we extract exact features from brain’s dynamic data and what kind of dynamics makes this
feature extraction more reliable? To answer these questions, in this study, we generated two kinds
of the brain dynamics computationally, i.e., spontaneous and task-evoked brain dynamics, and both
dynamics were applied to a fundamental technique for most feature extraction methods, that is, the
principal component analysis (PCA). We suggest that the task-evoked brain dynamics can give rise
to a feature space where different features, possibly related to personality traits, are classified more
robustly and may lead to a better brain-AI system |
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