Functional Classification of Spiking Signal Data Using Artificial Intelligence Techniques: A Review
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866915633529094144 |
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| author | Sharifrazi, Danial Javed, Nouman Joloudari, Javad Hassannataj Alizadehsani, Roohallah Paradkar, Prasad N. Tan, Ru-San Acharya, U. Rajendra Bhatti, Asim |
| author_facet | Sharifrazi, Danial Javed, Nouman Joloudari, Javad Hassannataj Alizadehsani, Roohallah Paradkar, Prasad N. Tan, Ru-San Acharya, U. Rajendra Bhatti, Asim |
| contents | Human brain neuron activities are incredibly significant nowadays. Neuronal behavior is assessed by analyzing signal data such as electroencephalography (EEG), which can offer scientists valuable information about diseases and human-computer interaction. One of the difficulties researchers confront while evaluating these signals is the existence of large volumes of spike data. Spikes are some considerable parts of signal data that can happen as a consequence of vital biomarkers or physical issues such as electrode movements. Hence, distinguishing types of spikes is important. From this spot, the spike classification concept commences. Previously, researchers classified spikes manually. The manual classification was not precise enough as it involves extensive analysis. Consequently, Artificial Intelligence (AI) was introduced into neuroscience to assist clinicians in classifying spikes correctly. This review discusses the importance and use of AI in spike classification, focusing on the recognition of neural activity noises. The task is divided into three main components: preprocessing, classification, and evaluation. Existing methods are introduced and their importance is determined. The review also highlights the need for more efficient algorithms. The primary goal is to provide a perspective on spike classification for future research and provide a comprehensive understanding of the methodologies and issues involved. The review organizes materials in the spike classification field for future studies. In this work, numerous studies were extracted from different databases. The PRISMA-related research guidelines were then used to choose papers. Then, research studies based on spike classification using machine learning and deep learning approaches with effective preprocessing were selected. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2409_17516 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Functional Classification of Spiking Signal Data Using Artificial Intelligence Techniques: A Review Sharifrazi, Danial Javed, Nouman Joloudari, Javad Hassannataj Alizadehsani, Roohallah Paradkar, Prasad N. Tan, Ru-San Acharya, U. Rajendra Bhatti, Asim Artificial Intelligence Machine Learning Neurons and Cognition Human brain neuron activities are incredibly significant nowadays. Neuronal behavior is assessed by analyzing signal data such as electroencephalography (EEG), which can offer scientists valuable information about diseases and human-computer interaction. One of the difficulties researchers confront while evaluating these signals is the existence of large volumes of spike data. Spikes are some considerable parts of signal data that can happen as a consequence of vital biomarkers or physical issues such as electrode movements. Hence, distinguishing types of spikes is important. From this spot, the spike classification concept commences. Previously, researchers classified spikes manually. The manual classification was not precise enough as it involves extensive analysis. Consequently, Artificial Intelligence (AI) was introduced into neuroscience to assist clinicians in classifying spikes correctly. This review discusses the importance and use of AI in spike classification, focusing on the recognition of neural activity noises. The task is divided into three main components: preprocessing, classification, and evaluation. Existing methods are introduced and their importance is determined. The review also highlights the need for more efficient algorithms. The primary goal is to provide a perspective on spike classification for future research and provide a comprehensive understanding of the methodologies and issues involved. The review organizes materials in the spike classification field for future studies. In this work, numerous studies were extracted from different databases. The PRISMA-related research guidelines were then used to choose papers. Then, research studies based on spike classification using machine learning and deep learning approaches with effective preprocessing were selected. |
| title | Functional Classification of Spiking Signal Data Using Artificial Intelligence Techniques: A Review |
| topic | Artificial Intelligence Machine Learning Neurons and Cognition |
| url | https://arxiv.org/abs/2409.17516 |