A Digital Filter Design for Optimized Brainwave Reception from Central Nervous System (CNS)

Authors

  • Dipannita Debasish Mondal Research Scholar, Electronics and Telecommunications Department, Lincoln University College, Malaysia Campus, Kuala Lumpur
  • Mukil Alagirisamy Associate Professor, Electrical and Electronics Engineering Department, Lincoln University College, Malaysia Campus, Kuala Lumpur

Keywords:

CNS- Central Nervous System, Finite Impulse Response (FIR) filters, Infinite Impulse Response (IIR) filters, BCI- Brain Computer Interface

Abstract

This abstract review an original research article on the topic of digital filter design for enhanced brain wave signals reception from the central nervous system (CNS) of the brain. The paper emphases on the development of an accurate digital filter to recover the reception and processing of brain wave signals, that is being considered to play a vital role in the brain functioning and diagnosing of suitable neurological disorders. The research paper reconnoitres advanced digital signal processing techniques and several other methodologies for effectively apprehending and analysing brain wave signals, considering all their unique characteristics and challenges. In this paper various filter design techniques, including Finite Impulse Response (FIR) filters and Infinite Impulse Response (IIR) filters are examined and evaluated for their aptness in optimizing CNS signal reception. The paper presents a detailed analysis of the optimization strategies working, such as Power Spectral Density, Power Spectral Efficiency, Upper cut-off frequency, Lower Cut-off frequency, Bandwidth and Delta power parameter calculations, to enhance the performance and accuracy of the digital filter. The research paper aims at discussing the potential applications of the developed digital filter in neuroscientific research and medical diagnosis, including brain-computer interfaces (BCIs), mental state monitoring and clinical neurology. The findings of the research contribute to the existing body of knowledge by providing visions into digital filter design for brain wave reception, thus facilitating developments in the field of neuroscience and refining healthcare outcomes.

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References

Janwadkar S,, Dhavse R.(2023). Power and area efficient FIR filter architecture in digital encephalography systems, e-Prime - Advances in Electrical Engineering, Electronics and Energy Volume 4, June 2023, 100148

Mondal,D, Alagirisamy,M.(2020). Brain Computer Interface (BCI): Mechanism and Challenges - A Survey, International Journal of Pharmaceutical Research, Jan - Mar 2020, Vol 12, Issue 1.

Chen, X., Wang, Y., Hu, Z., & Sun, Y. (2019). A novel denoising method based on multi-wavelet thresholding for brainwave signals. Journal of Medical Systems, 43(3), 63.

Nizam, Y., & Kumru, A. (2018). Digital filter design for removal of power-line interference from electroencephalography signals. Journal of Medical Signals and Sensors, 8(3), 163-169.

Patel, S. S., & Ghosh, D. (2018). Design of low pass FIR digital filter for removal of power line interference from EEG signal. Procedia Computer Science, 125, 629-635.

Rahal, M., Khalil, M., & Hamam, H. (2017). A high performance FIR filter design for EEG signal processing. In 2017 International Conference on Advanced Systems and Electric Technologies (IC_ASET) (pp. 312-317). IEEE.

Ashraf, F. U., Rehman, M. U., & Basit, A. (2016). Removal of baseline drift from ECG signal using adaptive FIR digital filter. In 2016 International Conference on Communication Technologies (ComTech) (pp. 121-125). IEEE.

Neshatvar, N., & Nourani, M. (2014). Optimized FIR filters for real-time EEG signal processing. In 2014 11th International Conference on Wearable and Implantable Body Sensor Networks (BSN) (pp. 88-92). IEEE.

Khan, M. T., & Rehman, A. (2013). Adaptive digital filtering of EEG signals using least mean squares technique. International Journal of Computer Applications, 80(9).

Gupta, S., Singh, N., & Gupta, A. (2012). Design and implementation of low power FIR filter for bio-potential signals. In 2012 Nirma University International Conference on Engineering (NUiCONE) (pp. 1-6). IEEE.

Singh, S., & Singh, Y. K. (2010). Design of FIR filters using time-frequency domain entropy and multi-objective optimization. In 2010 Annual IEEE India Conference (INDICON) (pp. 1-6). IEEE.

Krishnaveni, V., & Ramalingam, S. (2008). Adaptive FIR filter design for electroencephalogram. In 2008 International Conference on Computer and Electrical Engineering (pp. 335-339). IEEE.

Perez-Siguas, R. ., Matta-Solis, H. ., Matta-Solis, E. ., Matta-Perez, H. ., Cruzata-Martinez, A. ., & Meneses-Claudio, B. . (2023). Management of an Automatic System to Generate Reports on the Attendance Control of Teachers in a Educational Center. International Journal on Recent and Innovation Trends in Computing and Communication, 11(2), 20–26. https://doi.org/10.17762/ijritcc.v11i2.6106

Basaligheh, P. (2021). A Novel Multi-Class Technique for Suicide Detection in Twitter Dataset. Machine Learning Applications in Engineering Education and Management, 1(2), 13–20. Retrieved from http://yashikajournals.com/index.php/mlaeem/article/view/14

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Published

12.07.2023

How to Cite

Mondal, D. D. ., & Alagirisamy, M. . (2023). A Digital Filter Design for Optimized Brainwave Reception from Central Nervous System (CNS). International Journal of Intelligent Systems and Applications in Engineering, 11(9s), 207–216. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3109

Issue

Vol. 11 No. 9s (2023)

Section

Research Article

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