Dynamic systems and machine learning approach to online EEG signals processing for different applications

Zhuo Wang

doi:10.17918/00010608

Electroencephalography (EEG) is a non-invasive technique to record brain electrical activity. It involves placing electrodes on the scalp, which detects the electrical signals generated by neurons in the brain. EEG signals are complex and highly variable. And analyzing them requires sophisticated mathematical and computational techniques. Dynamic systems and machine learning approaches have been widely used in EEG signal processing due to their ability to capture complex and nonlinear relationships in the data. State-space-based dynamic systems theory involves modeling the EEG signal as a system of nonlinear differential equations, i.e., a state space, which can capture the underlying dynamics of the signal. The features extracted from dynamic systems models can then be used as inputs to machine learning algorithms such as decision trees, support vector machines, and deep neural networks. Machine learning algorithms can learn complex patterns and relationships in the data, which can be difficult to capture using traditional signal processing techniques. One of the most critical applications of EEG is in detecting epileptic seizures. Seizures are caused by abnormal electrical activity in the brain and can be challenging to detect using traditional clinical methods. EEG signals can provide a high temporal resolution and a direct measure of brain activity, making them an excellent tool for seizure detection. However, experienced physicians must spend considerable time and effort examining the EEG to make a diagnosis. Therefore, an automatic system that detects and annotates seizures by analyzing EEG would be beneficial. One advantage of automated EEG-based seizure detection is that it can provide real-time monitoring of brain activity, allowing for early detection and intervention. Furthermore, automated EEG-based seizure detection can customize patient treatment plans, optimizing their medication and reducing the risk of adverse effects. Another vital application of EEG is in fall detection, a widespread problem among older adults and people with mobility impairments. Falls can result in serious injuries, hospitalization, and reduced mobility, affecting the overall quality of life. Fall detection systems and devices, including inertial and ambient sensor-based systems, have been proposed to stress this issue. Although these methods can accurately detect abnormal activities, the challenge of distinguishing actual falls from activities of daily living (ADLs) persists. Wearable EEG devices may solve the challenge of accurately detecting real falls. Balance perturbations are accompanied by global cortical activation after perturbation onset. Evidence indicates that the disturbance's predictability modifies such EEG activity. Such findings highlight the potential of using EEG data with other sensors to develop more accurate and efficient fall detection systems, improving the safety and quality of life for older adults and other vulnerable populations. To sum up, this combined approach provides a powerful and flexible way to analyze and interpret EEG signals, allowing for real-time processing and improved performance compared to traditional signal processing techniques.

Dynamic systems and machine learning approach to online EEG signals processing for different applications

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Dynamic systems and machine learning approach to online EEG signals processing for different applications

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