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Journals / International Journal of Applied Mathematics Electronics and Computers / 2016 / Special Issue (2016)

Deep Belief Networks Based Brain Activity Classification Using EEG from Slow Cortical Potentials in Stroke

Gokhan ALTAN
Yakup KUTLU
Novruz ALLAHVERDİ
Pages
205–210
Publication Date
2016-12-01
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The proposed DBN-based brain activity classification has discriminated positivity and negativity tasks in stroke patients and has achieved high rates of 90.30%, 96.58%, and 91.15%, for sensitivity, selectivity, and accuracy, respectively.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:An electroencephalogram (EEG) is an electrical activity which is recorded from the scalp over the sensorimotor cortex during vigilance or sleeping conditions of subjects.
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Key Findings & Data:EEGs are recorded during eight SCP neurofeedback sessions from two stroke patients with a sampling rate of 256 Hz.
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Conclusion & Impact:The proposed DBN-based brain activity classification has discriminated positivity and negativity tasks in stroke patients and has achieved high rates of 90.30%, 96.58%, and 91.15%, for sensitivity, selectivity, and accuracy, respectively.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Deep Belief Networks · SCP · Slow Cortical potentials

Abstract

An electroencephalogram (EEG) is an electrical activity which is recorded from the scalp over the sensorimotor cortex during vigilance or sleeping conditions of subjects. It can be used to detect potential problems associated with brain disorders. The aim of this study is assessing the clinical usefulness of EEG which is recorded from slow cortical potentials (SCP) training in stroke patients using Deep belief network (DBN) which has a greedy layer wise training using Restricted Boltzmann Machines based unsupervised weight and bias evaluation and neural network based supervised training. EEGs are recorded during eight SCP neurofeedback sessions from two stroke patients with a sampling rate of 256 Hz. All EEGs are filtered with a low pass filter. Hilbert-Huang Transform is applied to the trails and various numbers of Instinct Mode Functions (IMFs) are obtained. High order statistics and standard statistics are extracted from IMFs to create the dataset. The proposed DBN-based brain activity classification has discriminated positivity and negativity tasks in stroke patients and has achieved high rates of 90.30%, 96.58%, and 91.15%, for sensitivity, selectivity, and accuracy, respectively.

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Cite This Article

ALTAN, G., KUTLU, Y., ALLAHVERDİ, N. (2016). Deep Belief Networks Based Brain Activity Classification Using EEG from Slow Cortical Potentials in Stroke. International Journal of Applied Mathematics Electronics and Computers, Special Issue (2016), 205–210. https://ajindex.com/articles/258184

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