Cloud-based mobile platform for EEG signal analysis

Dzaferovic, Emir; Vrtagic, Sabahudin; Bandić, Lejla; Kevrić, Jasmin; Subaşı, Abdülhamit; Qaisar, Saeed Mian

doi:10.1109/icedsa.2016.7818497

Cited by 11 publications

(3 citation statements)

References 16 publications

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“…A speech recognition and natural language processing system was provided relying on mobile cloud infrastructure to help caregivers record notes for patients [31]. A mobile cloud system was established [40] for Electroencephalography (EEG) analysis, based on which, further applications, such as Brain Computer Interface, could be deployed. Recently, a voice pathology detection system utilizing the mobile and IoT -Cloud technology was proposed [93], which was easy to use and can achieve a high level of detection accuracy.…”

Section: Telemonitoring and Biosignal Processingmentioning

confidence: 99%

An Overview of Mobile Cloud Computing for Pervasive Healthcare

Wang

Jin

2019

IEEE Access

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Mobile devices, along with wearable sensors, allow patients to access healthcare services from anywhere at any time. The longstanding constraints of computational capability and storage space on mobile devices can be alleviated by outsourcing computation-or data-intensive tasks to remote cloud centers. Thus, mobile cloud computing (MCC) has been recognized as a promising approach to provide pervasive healthcare services to people in their daily life. As the development and adoption of MCC techniques in healthcare, new optimization strategies have been explored and studied to help mobile cloud healthcare services to be deployed in a more effective and efficient manner. In this survey, we demonstrate how MCC techniques have been extensively deployed in various healthcare applications and, specifically, describe the general architecture and design considerations one should take into account while designing an MCC for healthcare scenarios. Given a large number of factors that may affect the performance of the MCC and even result in catastrophic consequences in healthcare, this paper presents the state-of-the-art optimization methods on the MCC for meeting the diverse priorities and achieving the optimal tradeoff among multiple objectives. Finally, the security and privacy issues of the MCC in healthcare are also discussed. INDEX TERMS Mobile cloud computing, pervasive healthcare. optimization, offloading, security and privacy. NOMENCLATURE

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Section: Telemonitoring and Biosignal Processingmentioning

confidence: 99%

An Overview of Mobile Cloud Computing for Pervasive Healthcare

Wang

Jin

2019

IEEE Access

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“…(Ahmed et al, 2017) came up with a wrist and chest wearable EEG device. (Džaferovic´ et al, 2016) and (Myers et al, 2016) proposed two different wearable EEG headsets that could send a signal to the android application using Bluetooth. (Pinho et al, 2017) proposed a wearable cap containing electrodes that can capture brain activities and send related EEG signals to IoT devices.…”

Section: Introductionmentioning

confidence: 99%

EpilNet: A Novel Approach to IoT based Epileptic Seizure Prediction and Diagnosis System using Artificial Intelligence

Gupta

Ranga

Agrawal

2022

ADCAIJ

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Epilepsy is one of the most occurring neurological diseases. The main characteristic of this disease is a frequent seizure, which is an electrical imbalance in the brain. It is generally accompanied by shaking of body parts and even leads (fainting). In the past few years, many treatments have come up. These mainly involve the use of anti-seizure drugs for controlling seizures. But in 70% of cases, these drugs are not effective, and surgery is the only solution when the condition worsens. So patients need to take care of themselves while having a seizure and be safe. Wearable electroencephalogram (EEG) devices have come up with the development in medical science and technology. These devices help in the analysis of brain electrical activities. EEG helps in locating the affected cortical region. The most important is that it can predict any seizure in advance on-site. This has resulted in a sudden increase in demand for effective and efficient seizure prediction and diagnosis systems. A novel approach to epileptic seizure prediction and diagnosis system “EpilNet” is proposed in the present paper. It is a one-dimensional (1D) convolution neural network. EpilNet gives the testing accuracy of 79.13% for five classes, leading to a significant increase of about 6-7% compared to related works. The developed Web API helps in bringing EpilNet into practical use. Thus, it is an integrated system for both patients and doctors. The system will help patients prevent injury or accidents and increase the efficiency of the treatment process by doctors in the hospitals.

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“…Moreover, the device gathers raw EEG signals, with the range of 512 Hz in the power spectrum (alpha, beta, delta, gamma, and theta). Importantly, the Mindwave sensor is positioned in the left side of the forehead, the position of Fp1 (Dzaferovic E. et al, 2017). The frontal lobe is a part that has FP1 to be obtained from by a sensor that the signals are obtained, and it is responsible for personality, behavior, emotions, judgment, planning, problemsolving, speaking and writing, body movement, intelligence, concentration, and self-awareness (Tonya, 2018).…”

Section: Introductionmentioning

confidence: 99%

Brain Waves Signal Modeling for Object Classification Using Random Forest Method

Mala¹,

Mohammad²

2022

SJUOZ

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In this research, the connection between human vision information and simultaneous brain signal is studied; to this end, an experiment has been made. Clearly, brain wave signals are captured in the situation that the participants are looking at the specific object. More precisely, the brain signals of 9 shapes are recorded for each participant. Also, 9 participants voluntarily have been involved in the experiment. Then, the collected signals are organised into training and testing groups. After that Random Forest classifier is used to classify the signals. The accuracy results demonstrate a connection between human vision information and simultaneous brain signal. Overall accuracy for all shapes as separated as per cases is 20.48%, and for shapes, numbers 6 and 8 are 55.34% 36.57%, respectively. It can be concluded that human brain signals can be categorised based on human vision inputs.

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Cloud-based mobile platform for EEG signal analysis

Cited by 11 publications

References 16 publications

An Overview of Mobile Cloud Computing for Pervasive Healthcare

An Overview of Mobile Cloud Computing for Pervasive Healthcare

EpilNet: A Novel Approach to IoT based Epileptic Seizure Prediction and Diagnosis System using Artificial Intelligence

Brain Waves Signal Modeling for Object Classification Using Random Forest Method

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