MULTI-PARAMETER BIOMEDICAL SENSOR-BASED MENTAL STATE CLASSIFICATION USING EEG AND DEEP LEARNING TECHNIQUES

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Year : 2026 | Volume : 17 | 02 | Page :
    By

    Ms. Asmita Sureshrao Gawande,

  • Amita M. Tembhare,

  • Dr.D.S.Dhote,

  1. Research Scholar, Department of Electronics, Brijlal Biyani Science College, Amravati, Maharashtra, India
  2. Research Scholar, Department of Electronics, Brijlal Biyani Science College, Amravati, Maharashtra, India
  3. Research Guide, Department of Electronics, Brijlal Biyani Science College, Amravati, Maharashtra, India

Abstract

With mental health concerns becoming increasingly widespread, there is a strong need for systems that can monitor conditions like stress, anxiety, and fatigue in a continuous and non- invasive manner. This research proposes a novel multi-parameter biomedical sensing framework for mental state classification by integrating electroencephalography (EEG) signals with physiological parameters, including body temperature acquired using LM35 sensors, heart rate from pulse sensors, and blood oxygen saturation (SpO₂) measurements. The system architecture incorporates a low-noise signal conditioning circuit for accurate acquisition of multimodal biosignals, followed by advanced feature extraction techniques such as Fast Fourier Transform (FFT) and wavelet transform to capture both temporal and spectral characteristics. The collected signals are processed using advanced techniques to accurately classify mental states, including normal stress, anxiety, and fatigue. Furthermore, the integration of Internet of Medical enables real-time data transmission, remote monitoring, and early alert generation for critical conditions. The proposed system emphasizes low-cost hardware implementation, energy efficiency, and scalability for wearable healthcare applications. Experimental evaluation demonstrates that the fusion of EEG with multi-parameter physiological signals significantly enhances classification accuracy compared to single-sensor approaches. The proposed framework provides a reliable and scalable solution for continuous mental health monitoring and has potential applications in telemedicine, smart healthcare systems, and assistive technologies.

Keywords: EEG (Electroencephalography), Mental State Classification, Multi-Parameter Biomedical Sensor System, LM35 Temperature Sensor, Pulse Sensor (Heart Rate), SpO₂ Monitoring, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Feature Extraction (FFT, Wavelet Transform), Wearable Healthcare System, Signal Conditioning Circuit

How to cite this article:
Ms. Asmita Sureshrao Gawande, Amita M. Tembhare, Dr.D.S.Dhote. MULTI-PARAMETER BIOMEDICAL SENSOR-BASED MENTAL STATE CLASSIFICATION USING EEG AND DEEP LEARNING TECHNIQUES. Current Trends in Signal Processing. 2026; 17(02):-.
How to cite this URL:
Ms. Asmita Sureshrao Gawande, Amita M. Tembhare, Dr.D.S.Dhote. MULTI-PARAMETER BIOMEDICAL SENSOR-BASED MENTAL STATE CLASSIFICATION USING EEG AND DEEP LEARNING TECHNIQUES. Current Trends in Signal Processing. 2026; 17(02):-. Available from: https://journals.stmjournals.com/ctsp/article=2026/view=246220


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Ahead of Print Subscription Review Article
Volume 17
02
Received 08/05/2026
Accepted 04/06/2026
Published 06/06/2026
Publication Time 29 Days
8

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