Paroxysmal diseases are characterized by sudden, episodic conditions that cause temporary changes in the body. Among them, epilepsy stands out for causing synchronous and uncontrolled neuronal discharges, resulting in recurrent and unprovoked seizures. These seizures may involve a loss or change in level of consciousness, abnormal movements or psychological symptoms. Other conditions, such as migraines, sleep disorders, and psychogenic non-epileptic seizures, may present similar symptoms but originate from different mechanisms and generally do not present abnormalities in brain activity.

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EEG System
Image reproduced from the article.

FACILITATING EPILEPSY DIAGNOSIS WITH A WIRELESS AND WEARABLE EEG SYSTEM

February 02, 25

Publication type: Article Summary
Original title: Wireless and Wearable EEG Acquisition Platform for Ambulatory Monitoring
Article publication date: May 2014
Source: ResearchGate
Authors: Francisco Pinho, José Higino Correia, Nuno Sousa, João José Cerqueira & Nuno Sérgio Dias

What is the goal, target audience, and areas of digital health it addresses?
     This research aims to develop a wearable wireless system for the acquisition of electroencephalogram (EEG) signals, allowing prolonged ambulatory monitoring of epilepsy. The target audience includes individuals with epilepsy or other paroxysmal diseases, as well as clinicians, neurologists and biomedical researchers. This project contributes to key areas of digital health, including neurotechnology, wearable devices, advanced signal processing technologies, remote patient monitoring, and real-time health data analysis.

What is the context?
     Paroxysmal diseases are characterized by sudden, episodic conditions that cause temporary changes in the body. Among them, epilepsy stands out for causing synchronous and uncontrolled neuronal discharges, resulting in recurrent and unprovoked seizures. These seizures may involve a loss or change in level of consciousness, abnormal movements or psychological symptoms. Other conditions, such as migraines, sleep disorders, and psychogenic non-epileptic seizures, may present similar symptoms but originate from different mechanisms and generally do not present abnormalities in brain activity.

     EEG is a non-invasive procedure that records brain electrical activity and identifies abnormal patterns, such as spikes and waves associated with epilepsy. It is essential for distinguishing epileptic from non-epileptic seizures, ensuring an accurate diagnosis in cases of overlapping symptoms. While short-term EEG is often the first step in diagnosing epilepsy, infrequent seizures or inconclusive results may require long-term monitoring to determine whether the origin of seizures is focal (in a specific region of the brain) or generalized (involving both hemispheres simultaneously), essential information for treatment.

     According to the American Clinical Neurophysiology Society, long-term EEG monitoring requires 32 to 64 channels for precise seizure localization, 24-hour continuous recording to capture rare events and, preferably, the inclusion of event detection algorithms for real-time analysis. Other relevant specifications include the ability to detect high frequencies to identify epileptic peaks, provide high signal resolution, and incorporate comfortable and easy-to-set-up electrodes to enhance usability and minimize preparation time.

What are the current approaches?
     The diagnosis of epilepsy is currently based on hospital monitoring, which combines simultaneous video recording and the use of EEG, connected by wires to a computer for analysis of electrical signals. This method is costly, time-consuming, and limits patient mobility.

     Ambulatory EEG monitoring offers a convenient alternative, enabling continuous brain activity recording during patients’ daily activities outside the hospital setting. These systems use scalp electrodes to capture analog EEG signals, which are amplified, converted into digital format by analog-to-digital converters (ADCs), and stored in a portable device before being wirelessly transmitted to a computer for visualization and processing.

     However, current wireless EEG systems face significant challenges, such as low channel density (usually between 3-16) and limitations in both high-frequency detection (< 512 Hz) and signal resolution (< 24 bits). Although some studies have explored wireless EEGs with higher channel density or the ability to detect higher frequencies, these features are rarely combined into a single device. Additionally, many systems use conductive gel-based electrodes, which require lengthy preparation and can dry out during long-term monitoring, leading to signal degradation and patient discomfort. Another issue is the use of limited bandwidth (e.g., bluetooth) and reliance on external devices for processing and storage, which may require proximity to the computer performing data analysis, restricting patient mobility.

What does innovation consist of? How is the impact of this study assessed?
     This research focuses on the development of a wireless and wearable EEG system, innovating in hardware, software, and operational functionalities. The proposed EEG platform stands out with its 32-channel configuration, high-frequency detection (256 to 1000 Hz) and high-resolution signal acquisition (24 bit per channel). To ensure data backup and offline analysis, the device includes an SD card. The device offers 2 operating modes via WiFi: continuous transmission for real-time monitoring, and event-driven transmission, which sends only relevant EEG segments after processing by epileptic event detection algorithms. Additionally, designed for long-term ambulatory monitoring, the device is powered by a 6600 mAh battery, providing up to 25 hours of continuous operation.

     The hardware architecture includes active dry electrodes, ADCs, and a central processing and transmission unit. These gel-free electrodes contain gold-plated phosphor-bronze contacts for greater stability. The hardware uses 4 ADCs, each responsible for 8 channels and the main amplification of the analog signal before digitization. The ADCs operate in cascade mode, sharing common signals and efficiently transmitting the digitized EEG data. The central unit integrates 2 software components: a kernel driver within the Linux operating system, which controls which of the four ADCs is active and manages digital data acquisition, and a userspace application for data processing, storage, and transmission.

     When new EEG data becomes available, the interrupt signal (DRDY-Data Ready) is triggered in the kernel driver, ensuring immediate priority for data processing. This pauses other tasks, stores the data in a readings memory buffer, groups it, and transfers it to the shared memory buffer, where it remains until the userspace application processes it. The userspace application begins by applying a Butterworth filter to remove noise before initiating processing, which includes feature extraction and event detection using epileptic algorithms. If an event is detected, the application automatically transmits the data via WiFi. Otherwise, the data is stored on the SD card, avoiding unnecessary transmissions and conserving energy. To facilitate real-time visualization and analysis, a C#-based application was developed within the .NET environment, allowing clinicians to review EEG signals efficiently on a computer.

     The system evaluation included technical performance metrics, analyzing the efficiency in the acquisition, processing, storage and transmission of EEG data in real time, as well as energy consumption and clinical usability evaluations to determine its feasibility in long-term ambulatory monitoring.

     In terms of technical performance, a key aspect analyzed was the task priority configuration, which affects the system’s ability to manage simultaneous operations. Two different configurations were tested: a default priority, where EEG data processing competes with other background tasks, and an immediate priority, where data acquisition receives the highest execution priority. Additionally, the efficiency of internal data transfer was assessed, focusing on the transition of EEG data from the readings memory buffer to the shared memory buffer, as well as retrieval from the shared memory buffer by the userspace application.  The storage performance was measured by analyzing the read/write speed of the SD card, ensuring timely EEG data recording and retrieval. The wireless transmission efficiency was compared between ad-hoc WiFi mode, which directly connects the EEG device to a local receiver (e.g., a computer), and infrastructure-based communication, where data is transmitted through a centralized network (router), allowing distribution across multiple devices. Finally, power consumption and battery life were measured under two conditions: WiFi enabled, for continuous data streaming, and with WiFi disabled, for offline recording.

     From a clinical usability perspective, the evaluation examined the system’s ability to accurately capture EEG signals under known physiological conditions. The device was tested for its ability to record alpha rhythms, a well-defined brain activity pattern that occurs when the eyes are closed and disappears when they are open. Additionally, it was evaluated for its sensitivity to muscle artifacts, such as those caused by jaw clenching, which can interfere with EEG recordings.

What are the main results? What is the impact of these results? What is the future of this approach?
     In terms of EEG data acquisition performance, assigning immediate priority to signal processing reduced response time by up to 763 microseconds at its peak compared to default priority. Additionally, data transfer from the readings memory buffer to the shared memory buffer took 122 to 244 microseconds, while the userspace application accessed the shared memory buffer in 275 microseconds. These speeds ensured fast and efficient processing. The SD card storage speed reached 470 Mbps, allowing for efficient data retrieval and real-time processing. These factors ensure that the system can handle large volumes of brain signal data smoothly and without interruptions. Wireless transmission efficiency further enhances performance, with ad-hoc WiFi mode enabling fast data transfer at 11 Mbps, while WiFi router transmission is limited to 2.6 Mbps due to network overload and routing delays, making direct wireless connections preferable for real-time EEG visualization. Although essential for real-time monitoring, the WiFi module significantly increases power consumption, requiring 500 mAh compared to 250 mAh when WiFi is off. However, with a 6600 mAh battery, the system can operate for 26.4 hours in continuous streaming mode and 52.8 hours in offline mode. In a real-world clinical setting, where EEG do not occur continuously, event detection mode can further extend battery life.

     The system demonstrates clinical feasibility, successfully detecting alpha rhythms under different conditions and distinguishing muscle artifacts, such as those caused by jaw clenching, confirming its suitability for diagnostic applications and its ability to differentiate neural signals from muscle interferences.

     These findings have a significant impact, as the system provides a viable alternative to traditional inpatient monitoring, enabling faster epilepsy diagnosis. Its portability and ease of use make it suitable for long-term monitoring and event-driven interventions, supporting personalized medicine approaches for chronic neurological conditions. By reducing reliance on expensive hospital setups, this technology democratizes access to advanced neurological diagnostics.

     The future of this technology involves miniaturization, power consumption optimization, and the integration of advanced analytics, including machine learning algorithms, to automate pattern recognition and anomaly detection, further enhancing its diagnostic capabilities. Beyond epilepsy, the system could be adapted for other applications like sleep studies. Ultimately, this technology represents a step toward better outcomes for patients with neurological disorders, also enhancing their quality of life even during diagnostic procedures.

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Know more here

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Home / Publications / Publication

EEG System
Image reproduced from the article.

FACILITATING EPILEPSY DIAGNOSIS WITH A WIRELESS AND WEARABLE EEG SYSTEM

February 02, 25

Publication type: Article Summary
Original title: Wireless and Wearable EEG Acquisition Platform for Ambulatory Monitoring
Article publication date: May 2014
Source: ResearchGate
Authors: Francisco Pinho, José Higino Correia, Nuno Sousa, João José Cerqueira & Nuno Sérgio Dias

What is the goal, target audience, and areas of digital health it addresses?
     This research aims to develop a wearable wireless system for the acquisition of electroencephalogram (EEG) signals, allowing prolonged ambulatory monitoring of epilepsy. The target audience includes individuals with epilepsy or other paroxysmal diseases, as well as clinicians, neurologists and biomedical researchers. This project contributes to key areas of digital health, including neurotechnology, wearable devices, advanced signal processing technologies, remote patient monitoring, and real-time health data analysis.

What is the context?
     Paroxysmal diseases are characterized by sudden, episodic conditions that cause temporary changes in the body. Among them, epilepsy stands out for causing synchronous and uncontrolled neuronal discharges, resulting in recurrent and unprovoked seizures. These seizures may involve a loss or change in level of consciousness, abnormal movements or psychological symptoms. Other conditions, such as migraines, sleep disorders, and psychogenic non-epileptic seizures, may present similar symptoms but originate from different mechanisms and generally do not present abnormalities in brain activity.

     EEG is a non-invasive procedure that records brain electrical activity and identifies abnormal patterns, such as spikes and waves associated with epilepsy. It is essential for distinguishing epileptic from non-epileptic seizures, ensuring an accurate diagnosis in cases of overlapping symptoms. While short-term EEG is often the first step in diagnosing epilepsy, infrequent seizures or inconclusive results may require long-term monitoring to determine whether the origin of seizures is focal (in a specific region of the brain) or generalized (involving both hemispheres simultaneously), essential information for treatment.

     According to the American Clinical Neurophysiology Society, long-term EEG monitoring requires 32 to 64 channels for precise seizure localization, 24-hour continuous recording to capture rare events and, preferably, the inclusion of event detection algorithms for real-time analysis. Other relevant specifications include the ability to detect high frequencies to identify epileptic peaks, provide high signal resolution, and incorporate comfortable and easy-to-set-up electrodes to enhance usability and minimize preparation time.

What are the current approaches?
     The diagnosis of epilepsy is currently based on hospital monitoring, which combines simultaneous video recording and the use of EEG, connected by wires to a computer for analysis of electrical signals. This method is costly, time-consuming, and limits patient mobility.

     Ambulatory EEG monitoring offers a convenient alternative, enabling continuous brain activity recording during patients’ daily activities outside the hospital setting. These systems use scalp electrodes to capture analog EEG signals, which are amplified, converted into digital format by analog-to-digital converters (ADCs), and stored in a portable device before being wirelessly transmitted to a computer for visualization and processing.

     However, current wireless EEG systems face significant challenges, such as low channel density (usually between 3-16) and limitations in both high-frequency detection (< 512 Hz) and signal resolution (< 24 bits). Although some studies have explored wireless EEGs with higher channel density or the ability to detect higher frequencies, these features are rarely combined into a single device. Additionally, many systems use conductive gel-based electrodes, which require lengthy preparation and can dry out during long-term monitoring, leading to signal degradation and patient discomfort. Another issue is the use of limited bandwidth (e.g., bluetooth) and reliance on external devices for processing and storage, which may require proximity to the computer performing data analysis, restricting patient mobility.

What does innovation consist of? How is the impact of this study assessed?
     This research focuses on the development of a wireless and wearable EEG system, innovating in hardware, software, and operational functionalities. The proposed EEG platform stands out with its 32-channel configuration, high-frequency detection (256 to 1000 Hz) and high-resolution signal acquisition (24 bit per channel). To ensure data backup and offline analysis, the device includes an SD card. The device offers 2 operating modes via WiFi: continuous transmission for real-time monitoring, and event-driven transmission, which sends only relevant EEG segments after processing by epileptic event detection algorithms. Additionally, designed for long-term ambulatory monitoring, the device is powered by a 6600 mAh battery, providing up to 25 hours of continuous operation.

     The hardware architecture includes active dry electrodes, ADCs, and a central processing and transmission unit. These gel-free electrodes contain gold-plated phosphor-bronze contacts for greater stability. The hardware uses 4 ADCs, each responsible for 8 channels and the main amplification of the analog signal before digitization. The ADCs operate in cascade mode, sharing common signals and efficiently transmitting the digitized EEG data. The central unit integrates 2 software components: a kernel driver within the Linux operating system, which controls which of the four ADCs is active and manages digital data acquisition, and a userspace application for data processing, storage, and transmission.

     When new EEG data becomes available, the interrupt signal (DRDY-Data Ready) is triggered in the kernel driver, ensuring immediate priority for data processing. This pauses other tasks, stores the data in a readings memory buffer, groups it, and transfers it to the shared memory buffer, where it remains until the userspace application processes it. The userspace application begins by applying a Butterworth filter to remove noise before initiating processing, which includes feature extraction and event detection using epileptic algorithms. If an event is detected, the application automatically transmits the data via WiFi. Otherwise, the data is stored on the SD card, avoiding unnecessary transmissions and conserving energy. To facilitate real-time visualization and analysis, a C#-based application was developed within the .NET environment, allowing clinicians to review EEG signals efficiently on a computer.

     The system evaluation included technical performance metrics, analyzing the efficiency in the acquisition, processing, storage and transmission of EEG data in real time, as well as energy consumption and clinical usability evaluations to determine its feasibility in long-term ambulatory monitoring.

     In terms of technical performance, a key aspect analyzed was the task priority configuration, which affects the system’s ability to manage simultaneous operations. Two different configurations were tested: a default priority, where EEG data processing competes with other background tasks, and an immediate priority, where data acquisition receives the highest execution priority. Additionally, the efficiency of internal data transfer was assessed, focusing on the transition of EEG data from the readings memory buffer to the shared memory buffer, as well as retrieval from the shared memory buffer by the userspace application.  The storage performance was measured by analyzing the read/write speed of the SD card, ensuring timely EEG data recording and retrieval. The wireless transmission efficiency was compared between ad-hoc WiFi mode, which directly connects the EEG device to a local receiver (e.g., a computer), and infrastructure-based communication, where data is transmitted through a centralized network (router), allowing distribution across multiple devices. Finally, power consumption and battery life were measured under two conditions: WiFi enabled, for continuous data streaming, and with WiFi disabled, for offline recording.

     From a clinical usability perspective, the evaluation examined the system’s ability to accurately capture EEG signals under known physiological conditions. The device was tested for its ability to record alpha rhythms, a well-defined brain activity pattern that occurs when the eyes are closed and disappears when they are open. Additionally, it was evaluated for its sensitivity to muscle artifacts, such as those caused by jaw clenching, which can interfere with EEG recordings.

What are the main results? What is the impact of these results? What is the future of this approach?
     In terms of EEG data acquisition performance, assigning immediate priority to signal processing reduced response time by up to 763 microseconds at its peak compared to default priority. Additionally, data transfer from the readings memory buffer to the shared memory buffer took 122 to 244 microseconds, while the userspace application accessed the shared memory buffer in 275 microseconds. These speeds ensured fast and efficient processing. The SD card storage speed reached 470 Mbps, allowing for efficient data retrieval and real-time processing. These factors ensure that the system can handle large volumes of brain signal data smoothly and without interruptions. Wireless transmission efficiency further enhances performance, with ad-hoc WiFi mode enabling fast data transfer at 11 Mbps, while WiFi router transmission is limited to 2.6 Mbps due to network overload and routing delays, making direct wireless connections preferable for real-time EEG visualization. Although essential for real-time monitoring, the WiFi module significantly increases power consumption, requiring 500 mAh compared to 250 mAh when WiFi is off. However, with a 6600 mAh battery, the system can operate for 26.4 hours in continuous streaming mode and 52.8 hours in offline mode. In a real-world clinical setting, where EEG do not occur continuously, event detection mode can further extend battery life.

     The system demonstrates clinical feasibility, successfully detecting alpha rhythms under different conditions and distinguishing muscle artifacts, such as those caused by jaw clenching, confirming its suitability for diagnostic applications and its ability to differentiate neural signals from muscle interferences.

     These findings have a significant impact, as the system provides a viable alternative to traditional inpatient monitoring, enabling faster epilepsy diagnosis. Its portability and ease of use make it suitable for long-term monitoring and event-driven interventions, supporting personalized medicine approaches for chronic neurological conditions. By reducing reliance on expensive hospital setups, this technology democratizes access to advanced neurological diagnostics.

     The future of this technology involves miniaturization, power consumption optimization, and the integration of advanced analytics, including machine learning algorithms, to automate pattern recognition and anomaly detection, further enhancing its diagnostic capabilities. Beyond epilepsy, the system could be adapted for other applications like sleep studies. Ultimately, this technology represents a step toward better outcomes for patients with neurological disorders, also enhancing their quality of life even during diagnostic procedures.

Would you like to know all the details?

Know more here
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