Ambulatory EEG NeuroMonitor platform for engagement studies of children with development delays

Mahajan, Ruhi; Consul-Pacareu, Sergi; Abusaud, Mohammed; Sahadat, Nazmus; Morshed, Bashir I.

doi:10.1117/12.2016205

Cited by 9 publications

(13 citation statements)

References 11 publications

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“…EEG sensors are miniature and lightweight for convenient ambulatory wearing and continuous sensing [14]. Such a system can be built with the new cutting-edge embedded technology consisting of an onboard microcontroller with dedicated input and output ports with specific functionalities that can operate independently while collecting data within natural settings [44][45][46].…”

Section: Electroencephalography (Eeg)mentioning

confidence: 99%

“…There are commercially available wireless EEG devices that capture data from 1 to 256 channels, and some of them are easy and convenient to put on. The feelings of sensors and the harness could be inconvenient, and relevant research is showing promise [46,52]. Usability and comfort are significant challenges to the potential for such technologies to be utilized in long duration BCI applications.…”

Section: Other Issuesmentioning

confidence: 99%

“…In addition to detection of such ictal episodes and Alzheimer's patient monitoring, algorithms have been proposed that utilizes analytics from phase synchrony of various brain lobes [96][97][98][99][100][101]. Recent findings of early detection of neurological disorders such as epilepsy, autism, and Alzheimer's disease as well as real-time monitoring of cognitive loads and collaborative learning show promise of BCI technologies as a viable medical tool of next generation [12,46,[102][103][104].…”

Section: Processing Of Brain Signalsmentioning

confidence: 99%

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A Brief Review of Brain Signal Monitoring Technologies for BCI Applications: Challenges and Prospects

Morshed¹

2014

J Bioeng Biomed Sci

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Significant strides have been made since 1940s for monitoring brain activities and utilizing the information for diagnosis, therapy, and control of robotic instruments including prosthetics. Monitoring brain activities with brain computer interfacing (BCI) technologies are of recent interest to due to the immense potential for various medical applications, particularly for many neurological disorder patients, and the emergence of technologies suitable for long duration BCI applications. Recent initiatives are geared towards transforming these clinic centric technologies to patient centric technologies by monitoring brain activities in practical settings. This paper briefly reviews current status of these technologies and relevant challenges. The technologies can be broadly classified into non-invasive (EEG, MEG, MRI) and invasive (Microelectrode, ECoG, MEA). Challenges to resolve include neuronal damage, neurotrophicity, usability and comfort.

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Section: Electroencephalography (Eeg)mentioning

confidence: 99%

Section: Other Issuesmentioning

confidence: 99%

Section: Processing Of Brain Signalsmentioning

confidence: 99%

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A Brief Review of Brain Signal Monitoring Technologies for BCI Applications: Challenges and Prospects

Morshed¹

2014

J Bioeng Biomed Sci

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“…ECG data were captured from subjects using our custombuilt ambulatory EEG/ECG embedded device (NeuroMonitor) [12,13]. Captured data were wirelessly (Bluetooth) transmitted to a remote computer for processing and analysis.…”

Section: Setup and Proceduresmentioning

confidence: 99%

“…The NeuroMonitor device is a small (5.58 cm x 2.03 cm x 0.91 cm) and light-weight (41.8 g), and contains hardware for ECG/EEG data collection [12,13]. For ECG data collection, an overall gain of Av = 93.28 and a bandwidth of …”

Section: A Devicementioning

confidence: 99%

Hardware-efficient robust biometric identification from 0.58 second template and 12 features of limb (Lead I) ECG signal using logistic regression classifier

Sahadat

Jacobs

Morshed

2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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The electrocardiogram (ECG), widely known as a cardiac diagnostic signal, has recently been proposed for biometric identification of individuals; however reliability and reproducibility are of research interest. In this paper, we propose a template matching technique with 12 features using logistic regression classifier that achieved high reliability and identification accuracy. Non-invasive ECG signals were captured using our custom-built ambulatory EEG/ECG embedded device (NeuroMonitor). ECG data were collected from healthy subjects (10), between 25-35 years, for 10 seconds per trial. The number of trials from each subject was 10. From each trial, only 0.58 seconds of Lead I ECG data were used as template. Hardware-efficient fiducial point detection technique was implemented for feature extraction. To obtain repeated random sub-sampling validation, data were randomly separated into training and testing sets at a ratio of 80:20. Test data were used to find the classification accuracy. ECG template data with 12 extracted features provided the best performance in terms of accuracy (up to 100%) and processing complexity (computation time of 1.2ms). This work shows that a single limb (Lead I) ECG can robustly identify an individual quickly and reliably with minimal contact and data processing using the proposed algorithm.

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Performance analysis of a DRL-less AFE for battery-powered wearable EEG

Mahajan

Morshed

2016

Measurement

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Ambulatory EEG NeuroMonitor platform for engagement studies of children with development delays

Cited by 9 publications

References 11 publications

A Brief Review of Brain Signal Monitoring Technologies for BCI Applications: Challenges and Prospects

A Brief Review of Brain Signal Monitoring Technologies for BCI Applications: Challenges and Prospects

Hardware-efficient robust biometric identification from 0.58 second template and 12 features of limb (Lead I) ECG signal using logistic regression classifier

Performance analysis of a DRL-less AFE for battery-powered wearable EEG

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