Analyzing the Impact of Binaural Beats on Anxiety Levels by a New Method Based on Denoised Harmonic Subtraction and Transient Temporal Feature Extraction

计算机科学 模式识别(心理学) 人工智能 特征提取 脑电图 减法 特征(语言学) 语音识别 维纳滤波器 焦虑 数学 心理学 语言学 算术 精神科 哲学
作者
Devika Rankhambe,Bharati Ainapure,Bhargav Appasani,Avireni Srinivasulu,Nicu Bizon
出处
期刊:Bioengineering [Multidisciplinary Digital Publishing Institute]
卷期号:11 (12): 1251-1251
标识
DOI:10.3390/bioengineering11121251
摘要

Anxiety is a widespread mental health issue, and binaural beats have been explored as a potential non-invasive treatment. EEG data reveal changes in neural oscillation and connectivity linked to anxiety reduction; however, harmonics introduced during signal acquisition and processing often distort these findings. Existing methods struggle to effectively reduce harmonics and capture the fine-grained temporal dynamics of EEG signals, leading to inaccurate feature extraction. Hence, a novel Denoised Harmonic Subtraction and Transient Temporal Feature Extraction is proposed to improve the analysis of the impact of binaural beats on anxiety levels. Initially, a novel Wiener Fused Convo Filter is introduced to capture spatial features and eliminate linear noise in EEG signals. Next, an Intrinsic Harmonic Subtraction Network is employed, utilizing the Attentive Weighted Least Mean Square (AW-LMS) algorithm to capture nonlinear summation and resonant coupling effects, effectively eliminating the misinterpretation of brain rhythms. To address the challenge of fine-grained temporal dynamics, an Embedded Transfo XL Recurrent Network is introduced to detect and extract relevant parameters associated with transient events in EEG data. Finally, EEG data undergo harmonic reduction and temporal feature extraction before classification with a cross-correlated Markov Deep Q-Network (DQN). This facilitates anxiety level classification into normal, mild, moderate, and severe categories. The model demonstrated a high accuracy of 95.6%, precision of 90%, sensitivity of 93.2%, and specificity of 96% in classifying anxiety levels, outperforming previous models. This integrated approach enhances EEG signal processing, enabling reliable anxiety classification and offering valuable insights for therapeutic interventions.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
杨19980625发布了新的文献求助10
2秒前
自觉问梅完成签到,获得积分10
2秒前
咚咚完成签到,获得积分10
2秒前
yyyyou完成签到,获得积分10
2秒前
ssqkeyan发布了新的文献求助50
3秒前
3秒前
3秒前
123完成签到,获得积分10
5秒前
瞎闹腾发布了新的文献求助10
5秒前
桐桐应助小张老师采纳,获得10
5秒前
6秒前
orixero应助伏特加采纳,获得10
6秒前
YYZX发布了新的文献求助10
7秒前
8秒前
杨19980625完成签到,获得积分10
9秒前
韦智杰发布了新的文献求助10
9秒前
12秒前
淡淡半莲发布了新的文献求助10
13秒前
隐形曼青应助瞎闹腾采纳,获得10
13秒前
小王发布了新的文献求助10
15秒前
15秒前
inspirx完成签到,获得积分10
15秒前
我是老大应助老实莫言采纳,获得10
16秒前
17秒前
18秒前
18秒前
19秒前
19秒前
领导范儿应助风清扬采纳,获得10
19秒前
20秒前
wayched完成签到,获得积分10
20秒前
英姑应助独特的半芹采纳,获得10
21秒前
小羊完成签到,获得积分10
21秒前
22秒前
123发布了新的文献求助10
22秒前
大蘑菇炒小蘑菇完成签到,获得积分10
23秒前
24秒前
哈哈发布了新的文献求助10
24秒前
l98916发布了新的文献求助10
24秒前
哈哈发布了新的文献求助10
25秒前
高分求助中
(应助此贴封号)【重要!!请各位详细阅读】【科研通的精品贴汇总】 10000
F-35B V2.0 How to build Kitty Hawk's F-35B Version 2.0 Model 2000
中国兽药产业发展报告 1000
Biodegradable Embolic Microspheres Market Insights 888
Quantum reference frames : from quantum information to spacetime 888
The Netter Collection of Medical Illustrations: Digestive System, Volume 9, Part III - Liver, Biliary Tract, and Pancreas (3rd Edition) 600
Pediatric Injectable Drugs 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 4450392
求助须知:如何正确求助?哪些是违规求助? 3918315
关于积分的说明 12161985
捐赠科研通 3568216
什么是DOI,文献DOI怎么找? 1959430
邀请新用户注册赠送积分活动 998797
科研通“疑难数据库(出版商)”最低求助积分说明 893880