Design and Evaluation of a Brain Signal-based Monitoring System for Differently-Abled People

Isaac Ritharson P, Shree Hari B, Madhavan G
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Abstract

The main goal of this paper is to design a system that will help actively monitor differently-abled patients in hospitals by capturing their brain signals which pass in the form of EEG signals. The signals are captured by observing the potential difference caused when an electric signal is passed during an instant time. Further, during active events, the changes are recorded and a range is assigned so that the values are mapped which would enable us to identify and recognize the state of a particular person. Later, this data is used to train a machine learning models which helps to classify a brain signal to a particular state that it resembles the most (using the pre-defined range as an output at the end). Further, tuning the model to improve generalization and hence concluding with the performance comparison of specialized machine learning algorithms to classify the input signals in terms of Accuracy, Precision, Recall, and F1 scores. This study also discusses about the challenges including the high level of noise in the EEG signals, which can significantly affect the accuracy and reliability of the data. Another challenge is the limited number of training examples, as collecting large amounts of EEG data from patients can be time-consuming and expensive. In future, the system can be enhanced by integrating smart IOT technologies such as sensors and buzzers to raise alerts to the concerned people.
基于脑信号的残疾人监测系统设计与评价
本文的主要目标是设计一个系统,通过捕获以脑电图信号形式传递的大脑信号,帮助医院主动监测不同功能的患者。通过观察电信号在瞬间通过时产生的电位差来捕获信号。此外,在活动事件期间,记录变化并分配范围,以便映射值,这将使我们能够识别和识别特定人员的状态。随后,这些数据被用于训练机器学习模型,该模型有助于将大脑信号分类为最相似的特定状态(使用预定义的范围作为最后的输出)。此外,调整模型以提高泛化,从而得出专门的机器学习算法的性能比较,以根据Accuracy, Precision, Recall和F1分数对输入信号进行分类。本研究还讨论了脑电信号中存在的高水平噪声等问题,这些问题会严重影响数据的准确性和可靠性。另一个挑战是训练样本数量有限,因为从患者那里收集大量脑电图数据既耗时又昂贵。未来,该系统可以通过集成传感器和蜂鸣器等智能物联网技术来增强,向相关人员发出警报。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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