Brain Signals Analysis Based Deep Learning Methods: Recent advances in the study of non-invasive brain signals

Essa, Almabrok; Kotte, Hari

doi:10.48550/arxiv.2201.04229

Cited by 2 publications

(2 citation statements)

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“…In contrast, deep learning algorithms can effectively learn underlying information across different subjects. Substantial efforts have been dedicated to developing EEG analysis algorithms that incorporate deep learning techniques, with promising outcomes (Bashivan et al, 2015 ; Lawhern et al, 2018 ; Zhang et al, 2018b ; Zhang P. et al, 2018 ; Essa and Kotte, 2021 ). A compact Convolutional Neural Network (CNN) is introduced in Lawhern et al ( 2018 ), which shows success on many different types of EEG paradigms.…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) cells have been suggested in Zhang et al ( 2018b ) to effectively exploit temporal dynamics. Moreover, several publications (Bashivan et al, 2015 ; Lawhern et al, 2018 ; Zhang et al, 2018b ; Zhang P. et al, 2018 ; Essa and Kotte, 2021 ) have integrated deep learning techniques with traditional spectral features. Despite deep learning's success in EEG analysis, only a limited number of studies have constructed a motor imagery classification model that can generalize to new subjects (Riyad et al, 2019 ; Zhu et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

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Revealing brain connectivity: graph embeddings for EEG representation learning and comparative analysis of structural and functional connectivity

Almohammadi,

Wang

2024

Front. Neurosci.

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This study employs deep learning techniques to present a compelling approach for modeling brain connectivity in EEG motor imagery classification through graph embedding. The compelling aspect of this study lies in its combination of graph embedding, deep learning, and different brain connectivity types, which not only enhances classification accuracy but also enriches the understanding of brain function. The approach yields high accuracy, providing valuable insights into brain connections and has potential applications in understanding neurological conditions. The proposed models consist of two distinct graph-based convolutional neural networks, each leveraging different types of brain connectivities to enhance classification performance and gain a deeper understanding of brain connections. The first model, Adjacency-based Convolutional Neural Network Model (Adj-CNNM), utilizes a graph representation based on structural brain connectivity to embed spatial information, distinguishing it from prior spatial filtering approaches dependent on subjects and tasks. Extensive tests on a benchmark dataset-IV-2a demonstrate that an accuracy of 72.77% is achieved by the Adj-CNNM, surpassing baseline and state-of-the-art methods. The second model, Phase Locking Value Convolutional Neural Network Model (PLV-CNNM), incorporates functional connectivity to overcome structural connectivity limitations and identifies connections between distinct brain regions. The PLV-CNNM achieves an overall accuracy of 75.10% across the 1–51 Hz frequency range. In the preferred 8–30 Hz frequency band, known for motor imagery data classification (including α, μ, and β waves), individual accuracies of 91.9%, 90.2%, and 85.8% are attained for α, μ, and β, respectively. Moreover, the model performs admirably with 84.3% accuracy when considering the entire 8–30 Hz band. Notably, the PLV-CNNM reveals robust connections between different brain regions during motor imagery tasks, including the frontal and central cortex and the central and parietal cortex. These findings provide valuable insights into brain connectivity patterns, enriching the comprehension of brain function. Additionally, the study offers a comprehensive comparative analysis of diverse brain connectivity modeling methods.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Revealing brain connectivity: graph embeddings for EEG representation learning and comparative analysis of structural and functional connectivity

Almohammadi,

Wang

2024

Front. Neurosci.

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Mind Controlled Bionic Arm with Sense of Touch [8 class version] v1

Shaw,

Sengupta,

Baswaraj Khaple

et al. 2024

Preprint

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Advancements in bionic technology are transforming the possibilities for restoring hand function in individuals with amputations or paralysis. This paper introduces a cost-effective bionic arm design that leverages mind-controlled functionality and integrates a sense of touch to replicate natural hand movements. The system utilizes a non-invasive EEG-based control mechanism, enabling users to operate the arm using brain signals processed into PWM commands for servo motor control of the bionic arm. Additionally, the design incorporates a touch sensor (tactile feedback) in the gripper, offering sensory feedback to enhance user safety and dexterity. The proposed bionic arm prioritizes three essential features: 1. Integrated Sensory Feedback: Providing users with a tactile experience to mimic the sense of touch (signals directly going to the brain). This capability is crucial for safe object manipulation by arm and preventing injuries 2. Mind-Control Potential: Harnessing EEG signals for seamless, thought-driven operation. 3. Non-Invasive Nature: Ensuring user comfort by avoiding invasive surgical procedures. This novel approach aims to deliver an intuitive, natural, and efficient solution for restoring complex hand functions.

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Brain Signals Analysis Based Deep Learning Methods: Recent advances in the study of non-invasive brain signals

Cited by 2 publications

References 39 publications

Revealing brain connectivity: graph embeddings for EEG representation learning and comparative analysis of structural and functional connectivity

Revealing brain connectivity: graph embeddings for EEG representation learning and comparative analysis of structural and functional connectivity

Mind Controlled Bionic Arm with Sense of Touch [8 class version] v1

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