Electroencephalography

Classifying multi-level stress responses from brain cortical EEG in Nurses and Non-health professionals using Machine Learning Auto Encoder

Author(s): Ashlesha Akella, Avinash Kumar Singh, Daniel Leong, Sara Lal, Phillip Newton, Roderick Clifton-Bligh, Craig Steven McLachlan, Sylvia Maria Gustin, Shamona Maharaj, Ty Lees, Zehong Cao, Chin-Teng Lin
Classifying multi-level stress responses from brain cortical EEG in Nurses and Non-health professionals using Machine Learning Auto Encoder 150 150 IEEE Journal of Translational Engineering in Health and Medicine (JTEHM)

Objective: Mental stress is a major problem in our society and has become an area of interest for many psychiatric researchers. One primary research focus area is the identification of…

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Massage Therapy’s Effectiveness on the decoding EEG rhythms of Left/Right Motor Imagery and Motion Execution in Patients with Skeletal Muscle Pain

Author(s): Huihui Li, Kai Fan, Junsong Ma, Bo Wang, Xiaohao Qiao, Yan Yan, Wenjing Du, Lei Wanga
Massage Therapy’s Effectiveness on the decoding EEG rhythms of Left/Right Motor Imagery and Motion Execution in Patients with Skeletal Muscle Pain 150 150 IEEE Journal of Translational Engineering in Health and Medicine (JTEHM)

Objective: Most of effectiveness assessments of the widely-used Massage therapy were based on subjective routine clinical assessment tools, such as Visual Analogue Scale (VAS) score. However, few studies demonstrated the…

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A deep convolutional neural network method to detect seizures and characteristic frequencies using epileptic electroencephalogram (EEG) data

Author(s): Md. Rashed-Al-Mahfuz, Mohammad Ali Moni, Shahadat Uddin, Salem A. Alyami, Mattew A. Summers, Valsamma Eapen
A deep convolutional neural network method to detect seizures and characteristic frequencies using epileptic electroencephalogram (EEG) data 150 150 IEEE Journal of Translational Engineering in Health and Medicine (JTEHM)

Background: Diagnosing epileptic seizures using electroencephalogram (EEG) in combination with deep learning computational methods has received much attention in recent years. However, to date, deep learning techniques in seizure detection…

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Low-Power and Low-Cost Dedicated Bit-Serial Hardware Neural Network for Epileptic Seizure Prediction System

Low-Power and Low-Cost Dedicated Bit-Serial Hardware Neural Network for Epileptic Seizure Prediction System

Author(s): Si Mon Kueh, Tom J. Kazmierski
Low-Power and Low-Cost Dedicated Bit-Serial Hardware Neural Network for Epileptic Seizure Prediction System 780 547 IEEE Journal of Translational Engineering in Health and Medicine (JTEHM)

  This paper presents results of using a simple bit-serial architecture as a method of designing an extremely low-power and low-cost neural network processor for epilepsy seizure prediction. The proposed…

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An Ultra Low Power Smart Headband for Real-time Epileptic Seizure Detection

An Ultra Low Power Smart Headband for Real-time Epileptic Seizure Detection

Author(s): Shih-Kai Lin, Istiqomah, Li-Chun Wang, Chin-Yew Lin, Herming Chiueh
An Ultra Low Power Smart Headband for Real-time Epileptic Seizure Detection 780 494 IEEE Journal of Translational Engineering in Health and Medicine (JTEHM)

     Abstract In this paper, the design of a smart headband for epileptic seizure detection is presented. The proposed headband consists of four key components: 1) an analog front-end circuitry,…

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Panel B shows 12 minutes of bipolar EEG from Fp2-F4 (1-70 Hz, 200 S/s). Panel A is the corresponding spectrogram. Panel E shows 30 seconds of EEG from Panel B at the onset of the generalized seizure (dashed line). Panels C, D, and F are the corresponding tEEG signals from Fp2’ (1-100 Hz, 200 S/s). Note the high gamma-band burst HFOs just prior to the partial seizure (highlighted by ellipse in panel C).

High-Frequency Oscillations Recorded on the Scalp of Patients with Epilepsy Using Tripolar Concentric Ring Electrodes

High-Frequency Oscillations Recorded on the Scalp of Patients with Epilepsy Using Tripolar Concentric Ring Electrodes 540 508 IEEE Journal of Translational Engineering in Health and Medicine (JTEHM)

Panel B shows 12 minutes of bipolar EEG from Fp2-F4 (1-70 Hz, 200 S/s). Panel A is the corresponding spectrogram. Panel E shows 30 seconds of EEGfrom Panel B at…

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