Brain-Computer Interface Research: A State-of-the-Art Summary 10

Guger, Christoph

doi:10.1007/978-3-030-79287-9_1

Cited by 5 publications

(7 citation statements)

References 8 publications

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“…By translating the extracted feature set to give it a proper class label, operational commands can be made. There are two categories of classification algorithms: linear and non-linear classifiers (Guger et al, 2021 ).…”

Section: Classical Methods For Eeg-based Bci Applicationsmentioning

confidence: 99%

Status of deep learning for EEG-based brain–computer interface applications

Hossain

Islam²,

Hossain³

et al. 2023

Front. Comput. Neurosci.

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In the previous decade, breakthroughs in the central nervous system bioinformatics and computational innovation have prompted significant developments in brain–computer interface (BCI), elevating it to the forefront of applied science and research. BCI revitalization enables neurorehabilitation strategies for physically disabled patients (e.g., disabled patients and hemiplegia) and patients with brain injury (e.g., patients with stroke). Different methods have been developed for electroencephalogram (EEG)-based BCI applications. Due to the lack of a large set of EEG data, methods using matrix factorization and machine learning were the most popular. However, things have changed recently because a number of large, high-quality EEG datasets are now being made public and used in deep learning-based BCI applications. On the other hand, deep learning is demonstrating great prospects for solving complex relevant tasks such as motor imagery classification, epileptic seizure detection, and driver attention recognition using EEG data. Researchers are doing a lot of work on deep learning-based approaches in the BCI field right now. Moreover, there is a great demand for a study that emphasizes only deep learning models for EEG-based BCI applications. Therefore, we introduce this study to the recent proposed deep learning-based approaches in BCI using EEG data (from 2017 to 2022). The main differences, such as merits, drawbacks, and applications are introduced. Furthermore, we point out current challenges and the directions for future studies. We argue that this review study will help the EEG research community in their future research.

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Section: Classical Methods For Eeg-based Bci Applicationsmentioning

confidence: 99%

Status of deep learning for EEG-based brain–computer interface applications

Hossain

Islam²,

Hossain³

et al. 2023

Front. Comput. Neurosci.

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“…As signals from the brain are unable to travel through the damaged area of the spinal cord, both motor and sensory functions get impaired by this neurological condition. Solutions to restore control of the limbs and trunk include rehabilitative training, therapies, cellular (transplantation of peripheral nerve cells, Schwann cells), and molecular approaches (axonal conduction enhancement) 1,2 . One such approach that is both novel and practical, as well as cost‐efficient which requires minimum invasive surgical intervention, is the brain‐computer interface (BCI).…”

Section: Introductionmentioning

confidence: 99%

“…Solutions to restore control of the limbs and trunk include rehabilitative training, therapies, cellular (transplantation of peripheral nerve cells, Schwann cells), and molecular approaches (axonal conduction enhancement). 1,2 One such approach that is both novel and practical, as well as cost-efficient which requires minimum invasive surgical intervention, is the braincomputer interface (BCI). BCI establishes a system of communication between computers and brain functionality.…”

Section: Introductionmentioning

confidence: 99%

“…AveragePooling3D again reduces obtained time series values by a factor of two. After time series information is reduced, localization information is learnt by Conv3D with kernel(1,3,3). This shows that convolution kernel (1,3,3) is applied to (time steps, localized row, localized col) while also connecting 8 time series reduction nodes to 8 spatial information nodes.As frequency and spatial information are learnt, learning the temporal dynamics of learnt frequency and spatial information is vital in EEG classification.…”

mentioning

confidence: 99%

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Attempted movement classification of spinal cord injured patients using hybrid CNN‐LSTM networks

Shrestha,

Rahman,

Saha

et al. 2024

Int J Imaging Syst Tech

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Individuals who are suffering from the most severe of motor disabilities can improve their quality of life by controlling and directing mechanical and electronic devices. As for Spinal Cord Injured (SCI) patients', attempted hand movements can be classified using electroencephalography (EEG). The research aims to develop a hybrid CNN‐LSTM (Convolutional Neural Network—Long Short Term Memory) architecture for multichannel EEG signal classification. It is a challenging task to classify real‐world multichannel EEG data from SCI patients. The proposed research preprocessed the EEG data to improve the signal‐to‐noise ratio and arranged for them to extract additional information from the data. The preprocessing step includes filtering, downsampling, and artifact removal, while the postprocessing step includes time‐frequency representation and spatial information encoding. A hybrid CNN‐LSTM is used for feature extraction and classification. The proposed method has been implemented on a dataset consisting of 5 different classes of attempted hand movements from 10 SCI patients. The average classification accuracy of 92.36% is achieved for 5‐class classification. To check the global validity of the proposed network, the BCI competition IV data is classified by the proposed method and has found 92.70% overall accuracy.

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“…The significance of network neuroscience has reached a global scale with a growing number of large-scale projects related to impactful topics such as brain disease, brain development, brain aging, and brain-computer interfacing (1)(2)(3). Maximizing the potential of these large projects to reach their goal depends on the data at one's disposal, which urges global collaboration, knowledge, and data sharing.…”

Section: Introductionmentioning

confidence: 99%

A Decentralized ComBat Algorithm and Applications to Functional Network Connectivity

et al. 2022

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Recent studies showed that working with neuroimage data collected from different research facilities or locations may incur additional source dependency, affecting the overall statistical power. This problem can be mitigated with data harmonization approaches. Recently, the ComBat method has become commonly adopted for various neuroimage modalities. While open neuroimaging datasets are becoming more common, a substantial amount of data is still unable to be shared for various reasons. In addition, current approaches require moving all the data to a central location, which requires additional resources and creates redundant copies of the same datasets. To address these issues, we propose a decentralized harmonization approach that does not create redundant copies of the original datasets and performs remote operations on the datasets separately without sharing any individual subject data, ensuring a certain level of privacy and reducing regulatory hurdles. We proposed a novel approach called “Decentralized ComBat” which can harmonize datasets separately without combining the datasets. We tested our model by harmonizing functional network connectivity datasets from two traumatic brain injury studies in a decentralized way. Also, we used simulations to analyze the performance and scalability of our model when the number of data collection sites increases. We compare the output with centralized ComBat and show that the proposed approach produces similar results, increasing the sensitivity of the functional network connectivity analysis and validating our approach. Simulations show that our model can be easily scaled to many more datasets based on the requirement. In sum, we believe this provides a powerful tool, further complementing open data and allowing for integrating public and private datasets.

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Brain-Computer Interface Research: A State-of-the-Art Summary 10

Cited by 5 publications

References 8 publications

Status of deep learning for EEG-based brain–computer interface applications

Status of deep learning for EEG-based brain–computer interface applications

Attempted movement classification of spinal cord injured patients using hybrid CNN‐LSTM networks

A Decentralized ComBat Algorithm and Applications to Functional Network Connectivity

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