Complex brain activity analysis and recognition based on multiagent methods

Zhang, Haolan; Liu, Jiming; Dowens, Margaret Gillon

doi:10.1002/cpe.5855

Cited by 4 publications

References 27 publications

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Combined LinkNet‐MBi‐LSTM for brain activity recognition with new Stockwell transform features

Takawale,

Paithane

2024

Intl J of Devlp Neuroscience

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Recognizing brain activity from EEG waves is an important field of study in biomedical engineering and neuroscience. Conventional approaches usually begin with signal processing techniques to extract features from the EEG data, and then machine learning algorithms are applied to classify the data. However, the spatial resolution of these EEG signals is low, which makes it difficult to pinpoint the exact location of the neural activity source in the brain. There are ongoing initiatives to use DL‐based brain activity recognition algorithms to overcome these constraints. Therefore, this work presents a novel hybrid framework for brain activity detection using the enhanced Stockwell transform and an EEG signal that is called LinkNet and modified bidirectional–long short‐term memory (LN‐MBi‐LSTM) model. This framework follows a methodical approach that includes stages for feature extraction, brain activity recognition and preprocessing. Firstly, the improved Weiner filtering (IWF) approach is used to preprocess the EEG input signal. The relevant features are then extracted using a feature extraction technique from the preprocessed EEG signal. To identify the brain activity, these recovered feature sets are subsequently processed separately using LinkNet and modified bidirectional–long short‐term memory (MBi‐LSTM). A thorough analysis that takes into account both simulation and experimental calculations is part of the validation process for the LN‐MBi‐LSTM model. Finally, this study demonstrates the therapeutic potential of the LN‐MBi‐LSTM framework by presenting a strong and verified model for brain activity recognition. With the highest precision of 0.997, the LinkNet‐MBi‐LSTM model distinguishes itself from other models and confirms its exceptional capacity to produce accurate positive predictions.

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Combined LinkNet‐MBi‐LSTM for brain activity recognition with new Stockwell transform features

Takawale,

Paithane

2024

Intl J of Devlp Neuroscience

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Human-Computer Interaction Model for Brainstorming Based on Extenics

Zhang

2021

Brain Informatics

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Ictal-onset localization through effective connectivity analysis based on RNN-GC with intracranial EEG signals in patients with epilepsy

Wang,

Liu,

Yang

2024

Brain Inf.

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Epilepsy is one of the most common clinical diseases of the nervous system. The occurrence of epilepsy will bring many serious consequences, and some patients with epilepsy will develop drug-resistant epilepsy. Surgery is an effective means to treat this kind of patients, and lesion localization can provide a basis for surgery. The purpose of this study was to explore the functional types and connectivity evolution patterns of relevant regions of the brain during seizures. We used intracranial EEG signals from patients with epilepsy as the research object, and the method used was GRU-GC. The role of the corresponding area of each channel in the seizure process was determined by the introduction of group analysis. The importance of each area was analysed by introducing the betweenness centrality and PageRank centrality. The experimental results show that the classification method based on effective connectivity has high accuracy, and the role of the different regions of the brain could also change during the seizures. The relevant methods in this study have played an important role in preoperative assessment and revealing the functional evolution patterns of various relevant regions of the brain during seizures.

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Complex brain activity analysis and recognition based on multiagent methods

Cited by 4 publications

References 27 publications

Combined LinkNet‐MBi‐LSTM for brain activity recognition with new Stockwell transform features

Combined LinkNet‐MBi‐LSTM for brain activity recognition with new Stockwell transform features

Human-Computer Interaction Model for Brainstorming Based on Extenics

Ictal-onset localization through effective connectivity analysis based on RNN-GC with intracranial EEG signals in patients with epilepsy

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