Department of Computer Science and Engineering, ASET, Amity University Rajasthan, Jaipur, India.
School of Computing & Information Technology, Manipal University Jaipur, Jaipur, Rajasthan 303007, India.
Comput Intell Neurosci. 2022 Apr 28;2022:9737511. doi: 10.1155/2022/9737511. eCollection 2022.
The brain is the most complex organ in the human body, and it is also the most complex organ in the whole biological system, making it the most complex organ on the planet. According to the findings of current studies, modern study that properly characterises the EEG data signal provides a clear classification accuracy of human activities which is distinct from previous research. Various brain wave patterns related to common activities such as sleeping, reading, and watching a movie may be found in the Electroencephalography (EEG) data that has been collected. As a consequence of these activities, we accumulate numerous sorts of emotion signals in our brains, including the Delta, Theta, and Alpha bands. These bands will provide different types of emotion signals in our brain as a result of these activities. As a consequence of the nonstationary nature of EEG recordings, time-frequency-domain techniques, on the other hand, are more likely to provide good findings. The ability to identify different neural rhythm scales using time-frequency representation has also been shown to be a legitimate EEG marker; this ability has also been demonstrated to be a powerful tool for investigating small-scale neural brain oscillations. This paper presents the first time that a frequency analysis of EEG dynamics has been undertaken. An augmenting decomposition consisting of the "Versatile Inspiring Wavelet Transform" and the "Adaptive Wavelet Transform" is used in conjunction with the EEG rhythms that were gathered to provide adequate temporal and spectral resolutions. Children's wearable sensors are being used to collect data from a number of sources, including the Internet. The signal is conveyed over the Internet of Things (IoT). Specifically, the suggested approach is assessed on two EEG datasets, one of which was obtained in a noisy (i.e., nonshielded) environment and the other was recorded in a shielded environment. The results illustrate the resilience of the proposed training strategy. Therefore, our method contributes to the identification of specific brain activity in children who are taking part in the research as a result of their participation. On the basis of several parameters such as filtering response, accuracy, precision, recall, and F-measure, the MATLAB simulation software was used to evaluate the performance of the proposed system.
大脑是人体中最复杂的器官，也是整个生物系统中最复杂的器官，使其成为地球上最复杂的器官。根据目前的研究结果，适当描述 EEG 数据信号的现代研究为人类活动提供了明确的分类准确性，与之前的研究不同。在已经收集的脑电图 (EEG) 数据中，可以找到与睡眠、阅读和看电影等常见活动相关的各种脑电波模式。由于这些活动，我们的大脑中积累了许多种情绪信号，包括 Delta、Theta 和 Alpha 波段。由于这些活动，这些波段会在我们的大脑中提供不同类型的情绪信号。由于 EEG 记录的非平稳性质，另一方面，时频域技术更有可能提供良好的发现。使用时频表示来识别不同的神经节律尺度的能力也被证明是 EEG 的有效标记；这种能力也被证明是研究小尺度神经脑震荡的有力工具。本文首次对 EEG 动力学进行了频率分析。使用“通用激励小波变换”和“自适应小波变换”的增强分解与 EEG 节律相结合，以提供足够的时间和频谱分辨率。儿童可穿戴传感器正被用于从多个来源（包括互联网）收集数据。信号通过物联网 (IoT) 传输。具体来说，所提出的方法在两个 EEG 数据集上进行了评估，一个是在嘈杂（即无屏蔽）环境中获得的，另一个是在屏蔽环境中记录的。结果表明了所提出的训练策略的弹性。因此，我们的方法有助于识别参与研究的儿童的特定大脑活动，因为他们的参与。基于过滤响应、准确性、精度、召回率和 F-measure 等几个参数，使用 MATLAB 模拟软件评估了所提出系统的性能。
