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Start Over Author "Gil Issachar" Topic neurofeedback
2 results on '"Gil Issachar"'

1. Neurofeedback Therapy for Sensory Over-Responsiveness—A Feasibility Study

Author

Ruba Hamed, Limor Mizrachi, Yelena Granovsky, Gil Issachar, Shlomit Yuval-Greenberg, and Tami Bar-Shalita

Subjects
sensory processing, sensory modulation dysfunction, neuromodulation, neurofeedback, pain sensitivity, life satisfaction, Chemical technology, TP1-1185
Abstract

Background: Difficulty in modulating multisensory input, specifically the sensory over-responsive (SOR) type, is linked to pain hypersensitivity and anxiety, impacting daily function and quality of life in children and adults. Reduced cortical activity recorded under resting state has been reported, suggestive of neuromodulation as a potential therapeutic modality. This feasibility study aimed to explore neurofeedback intervention in SOR. Methods: Healthy women with SOR (n = 10) underwent an experimental feasibility study comprising four measurement time points (T1—baseline; T2—preintervention; T3—postintervention; T4—follow-up). Outcome measures included resting-state EEG recording, in addition to behavioral assessments of life satisfaction, attaining functional goals, pain sensitivity, and anxiety. Intervention targeted the upregulation of alpha oscillatory power over ten sessions. Results: No changes were detected in all measures between T1 and T2. Exploring the changes in brain activity between T2 and T4 revealed power enhancement in delta, theta, beta, and gamma oscillatory bands, detected in the frontal region (p = 0.03–d = 0.637–1.126) but not in alpha oscillations. Furthermore, a large effect was found in enhancing life satisfaction and goal attainment (Cohen’s d = 1.18; 1.04, respectively), and reduced pain sensitivity and anxiety trait (Cohen’s d = 0.70). Conclusion: This is the first study demonstrating the feasibility of neurofeedback intervention in SOR.

Published
2022
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2. Design and Implementation of a Novel Subject-Specific Neurofeedback Evaluation and Treatment System

Author

Tami Bar-Shalita, Yair Baruch, Bar Horing, Sigal Portnoy, and Gil Issachar

Subjects
Male, Computer science, Models, Neurological, 0206 medical engineering, Biomedical Engineering, Feature selection, Sensory system, 02 engineering and technology, Electroencephalography, Session (web analytics), Machine Learning, Staring, Event-related potential, medicine, Humans, medicine.diagnostic_test, business.industry, Signal Processing, Computer-Assisted, Pattern recognition, Neurofeedback, Brain Waves, 020601 biomedical engineering, Computer game, Female, Artificial intelligence, business
Abstract

Electroencephalography (EEG)-based neurofeedback (NF) is a safe, non-invasive, non-painful method for treating various conditions. Current NF systems enable the selection of only one NF parameter, so that two parameters cannot be feedback simultaneously. Consequently, the ability to individually-tailor the treatment to a patient is limited, and treatment efficiency may therefore be compromised. We aimed to design, implement and test an all-in-one, novel, computerized platform for closed-loop NF treatment, based on principles from learning theories. Our prototype performs numeric evaluation based on quantifying resting EEG and event-related EEG responses to various sensory stimuli. The NF treatment was designed according to principles of efficient learning, and implemented as a gradual, patient-adaptive 1D or 2D computer game, that utilizes automatic EEG feature extraction. Verification was performed as we compared the mean area under curve (AUC) of the theta band of a dozen subjects staring at a wall or performing the NF. Most of the subjects (75%) increased their theta band AUC during the NF session compared with the trial staring at the wall (p = 0.041). Our system enables multiple feature selection and its machine learning capabilities allow an accurate discovery of patient-specific biomarkers and treatment targets. Its novel characteristics may allow for improved evaluation of patients and treatment outcomes.

Published
2019

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