ArticleFrontiers in neuroscience2019
Brain-Computer Interface (BCI) Applications in Mapping of Epileptic Brain Networks Based on Intracranial-EEG: An Update.
Article in Frontiers in neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Large Animal Studies to Reduce the Foreign Body Reaction in Brain-Computer Interfaces: A Systematic Review.Biosensors · 2021Pooled it
- A multi-dimensional CNN-Bi-GRU for IoT-based brain-computer interface in early epileptic seizure detection.Biology methods & protocols · 2026Article
- Editorial: Exploring the future of neurology: how AI is revolutionizing diagnoses, treatments, and beyond.Frontiers in neurology · 2025Article
- A Method to Extract Task-Related EEG Feature Based on Lightweight Convolutional Neural Network.Neuroscience bulletin · 2024Article
- Electroencephalography-Based Brain-Computer Interfaces in Rehabilitation: A Bibliometric Analysis (2013-2023).Sensors (Basel, Switzerland) · 2024Review
- Qualitative studies involving users of clinical neurotechnology: a scoping review.BMC medical ethics · 2024Article
- Optimizing spatial accuracy in electroencephalography reconstruction through diffuse optical tomography priors in the auditory cortex.Biomedical optics express · 2024Article
- Editorial: Neural computations for brain machine interface applications.Frontiers in human neuroscience · 2023Article
- Passive localization of the central sulcus during sleep based on intracranial EEG.Cerebral cortex (New York, N.Y. : 1991) · 2022Article
- Event-Related Potential-Based Brain-Computer Interface Using the Thai Vowels' and Numerals' Auditory Stimulus Pattern.Sensors (Basel, Switzerland) · 2022Article
- Steady-State Visual Evoked Potential-Based Brain-Computer Interface Using a Novel Visual Stimulus with Quick Response (QR) Code Pattern.Sensors (Basel, Switzerland) · 2022Article
- Editorial: Magnetoencephalography: Methodological innovation paves the way for scientific discoveries and new clinical applications.Frontiers in neurology · 2022Article
- Vector Phase Analysis Approach for Sleep Stage Classification: A Functional Near-Infrared Spectroscopy-Based Passive Brain-Computer Interface.Frontiers in human neuroscience · 2021Article
- The value of intra-operative electrographic biomarkers for tailoring during epilepsy surgery: from group-level to patient-level analysis.Scientific reports · 2020Article
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Abstract
The main applications of the Brain-Computer Interface (BCI) have been in the domain of rehabilitation, control of prosthetics, and in neuro-feedback. Only a few clinical applications presently exist for the management of drug-resistant epilepsy. Epilepsy surgery can be a life-changing procedure in the subset of millions of patients who are medically intractable. Recording of seizures and localization of the Seizure Onset Zone (SOZ) in the subgroup of "surgical" patients, who require intracranial-EEG (icEEG) evaluations, remain to date the best available surrogate marker of the epileptogenic tissue. icEEG presents certain risks and challenges making it a frontier that will benefit from optimization. Despite the presentation of several novel biomarkers for the localization of epileptic brain regions (HFOs-spikes vs. Spikes for instance), integration of most in practices is not at the prime time as it requires a degree of knowledge about signal and computation. The clinical care remains inspired by the original practices of recording the seizures and expert visual analysis of rhythms at onset. It is becoming increasingly evident, however, that there is more to infer from the large amount of EEG data sampled at rates in the order of less than 1 ms and collected over several days of invasive EEG recordings than commonly done in practice. This opens the door for interesting areas at the intersection of neuroscience, computation, engineering and clinical care. Brain-Computer interface (BCI) has the potential of enabling the processing of a large amount of data in a short period of time and providing insights that are not possible otherwise by human expert readers. Our practices suggest that implementation of BCI and Real-Time processing of EEG data is possible and suitable for most standard clinical applications, in fact, often the performance is comparable to a highly qualified human readers with the advantage of producing the results in real-time reliably and tirelessly. This is of utmost importance in specific environments such as in the operating room (OR) among other applications. In this review, we will present the readers with potential targets for BCI in caring for patients with surgical epilepsy.
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