Design of EEG data acquisition system based on Raspberry Pi 3 for acute ischemic stroke identification

Arif, Rizki; Wijaya, Sastra Kusuma; Prawito,; Gani, Hendra Saputra

doi:10.1109/icsigsys.2018.8372771

Cited by 15 publications

(5 citation statements)

References 13 publications

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“…In an EEG system, the processor circuit is a core component that enables real-time processing and analysis of raw signals obtained from the EEG acquisition device, laying the foundation for subsequent data-processing and research. Commonly used processor circuits include a field-programmable gate array (FPGA), digital signal processors (DSP), and central processing unit (CPU) [70].…”

Section: Processor Circuitmentioning

confidence: 99%

Application and Development of EEG Acquisition and Feedback Technology: A Review

Qin,

Zhang,

Zhang

et al. 2023

Biosensors

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This review focuses on electroencephalogram (EEG) acquisition and feedback technology and its core elements, including the composition and principles of the acquisition devices, a wide range of applications, and commonly used EEG signal classification algorithms. First, we describe the construction of EEG acquisition and feedback devices encompassing EEG electrodes, signal processing, and control and feedback systems, which collaborate to measure faint EEG signals from the scalp, convert them into interpretable data, and accomplish practical applications using control feedback systems. Subsequently, we examine the diverse applications of EEG acquisition and feedback across various domains. In the medical field, EEG signals are employed for epilepsy diagnosis, brain injury monitoring, and sleep disorder research. EEG acquisition has revealed associations between brain functionality, cognition, and emotions, providing essential insights for psychologists and neuroscientists. Brain–computer interface technology utilizes EEG signals for human–computer interaction, driving innovation in the medical, engineering, and rehabilitation domains. Finally, we introduce commonly used EEG signal classification algorithms. These classification tasks can identify different cognitive states, emotional states, brain disorders, and brain–computer interface control and promote further development and application of EEG technology. In conclusion, EEG acquisition technology can deepen the understanding of EEG signals while simultaneously promoting developments across multiple domains, such as medicine, science, and engineering.

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Section: Processor Circuitmentioning

confidence: 99%

Application and Development of EEG Acquisition and Feedback Technology: A Review

Qin,

Zhang,

Zhang

et al. 2023

Biosensors

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“…In-home-PSG is a valid alternative to in-sleep lab PSG without compromising the gold standard aspect. In-home scenarios, Raspberry Pi (R-Pi) is a popular choice to capture EEG signals; it facilitates designing low-cost and affordable sleep monitoring systems [ 6 , 7 ]. The captured signals are processed and classified as distinct sleep stages using automated sleep-stage classification (ASSC) systems.…”

Section: Introductionmentioning

confidence: 99%

An Autonomous Sleep-Stage Detection Technique in Disruptive Technology Environment

Radhakrishnan,

Ezra,

Jebadurai

et al. 2024

Sensors

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Autonomous sleep tracking at home has become inevitable in today’s fast-paced world. A crucial aspect of addressing sleep-related issues involves accurately classifying sleep stages. This paper introduces a novel approach PSO–XGBoost, combining particle swarm optimisation (PSO) with extreme gradient boosting (XGBoost) to enhance the XGBoost model’s performance. Our model achieves improved overall accuracy and faster convergence by leveraging PSO to fine-tune hyperparameters. Our proposed model utilises features extracted from EEG signals, spanning time, frequency, and time–frequency domains. We employed the Pz-oz signal dataset from the sleep-EDF expanded repository for experimentation. Our model achieves impressive metrics through stratified-K-fold validation on ten selected subjects: 95.4% accuracy, 95.4% F1-score, 95.4% precision, and 94.3% recall. The experiment results demonstrate the effectiveness of our technique, showcasing an average accuracy of 95%, outperforming traditional machine learning classifications. The findings revealed that the feature-shifting approach supplements the classification outcome by 3 to 4 per cent. Moreover, our findings suggest that prefrontal EEG derivations are ideal options and could open up exciting possibilities for using wearable EEG devices in sleep monitoring. The ease of obtaining EEG signals with dry electrodes on the forehead enhances the feasibility of this application. Furthermore, the proposed method demonstrates computational efficiency and holds significant value for real-time sleep classification applications.

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“…Others focus on analog-todigital conversion and transmission of EEG signals [8], [9], [10], [11], [12], [13], [14], [15]. Some of them also design a graphical user interface (GUI) for signal processing and analysis [16], [17]. Creamino [5] is capable of processing multiple EEG channels in real-time and has the characteristics of cost-effectiveness.…”

Section: Introductionmentioning

confidence: 99%

BEATS: An Open-Source, High-Precision, Multi-Channel EEG Acquisition Tool System

Zhu¹,

Zheng²,

Shen³

et al. 2022

Preprint

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Stable and accurate electroencephalogram (EEG) signal acquisition is fundamental in non-invasive brain-computer interface (BCI) technology. Commonly used EEG acquisition system's hardware and software are usually closed-source. Its inability to flexible expansion and secondary development is a major obstacle to real-time BCI research. This paper presents an open-source, highprecision, multi-channel EEG Acquisition Tool System developed by Beijing University of Posts and Telecommunications named BEATS. It implements a comprehensive system from hardware to software, composes of analog front-end, microprocessor, and software platform. BEATS is capable of collecting multi-channel micro-volt EEG signals up to 4000 Hz with wireless transmission. And it adopts a pluggable structure and easy-to-access materials, which can easily support rapid prototyping, portability, and scalability. Some underlying techniques like direct memory access, interrupt, first in first out are used to ensure the precision and stability of the program at the microsecond level. Compared to state-of-the-art systems, BEATS maintains a relatively high channel number when acquiring data at a high sampling rate, while being quick to set up and use, making it ideal for a wide range of BCI scenarios or long-term daily monitoring. Schematics, source code, and other materials of BEATS are available at https://github.com/bingzant/BEATS.

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Design of EEG data acquisition system based on Raspberry Pi 3 for acute ischemic stroke identification

Cited by 15 publications

References 13 publications

Application and Development of EEG Acquisition and Feedback Technology: A Review

Application and Development of EEG Acquisition and Feedback Technology: A Review

An Autonomous Sleep-Stage Detection Technique in Disruptive Technology Environment

BEATS: An Open-Source, High-Precision, Multi-Channel EEG Acquisition Tool System

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