Author
Jwalajose, Dr. B. Suresh Kumar
Keywords
Artificial Intelligence; Dream Recording; Neuroscience; Wearable Devices; Sleep Research.
Abstract
This study explores the feasibility of utilizing artificial intelligence (AI) technology for the recording and analysis of dreams. Participants were equipped with wearable devices embedded with AI algorithms designed to detect and record dream-related brain activity during sleep [1]. Results indicate a promising potential for AI-based dream recording methods, offering valuable insights into the nature of dreams and their neural correlates. Future research directions and implications are discussed.
References
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[4] Ildar Rakhmatulin, M.-S. Dao, Amir Nassibi, and D. Mandic, “Exploring Convolutional Neural Network Architectures for EEG Feature Extraction,” Sensors, vol. 24, no. 3, pp. 877–877, Jan. 2024, doi: https://doi.org/10.3390/s24030877.
[5] Johns Hopkins Medicine, “Electroencephalogram (EEG),” John Hopkins Medicine, 2019. https://www.hopkinsmedicine.org/ health/treatment-tests-and-therapies/electroencephalogram-eeg
[6] A. Chaddad, Y. Wu, Reem Kateb, and A. Bouridane, “Electroencephalography Signal Processing: A Comprehensive Review and Analysis of Methods and Techniques,” Sensors, vol. 23, no. 14, pp. 6434–6434, Jul. 2023, doi: https://doi.org/10.3390/s23146434.
[7] X. Jiang, G.-B. Bian, and Z. Tian, “Removal of Artifacts from EEG Signals: A Review,” Sensors (Basel, Switzerland), vol. 19, no. 5, p. 987, 2019, doi: https://doi.org/10.3390/s19050987.
[8] H. U. Amin, W. Mumtaz, A. R. Subhani, M. N. M. Saad, and A. S. Malik, “Classification of EEG Signals Based on Pattern Recognition Approach,” Frontiers in Computational Neuroscience, vol. 11, Nov. 2017, doi: https://doi.org/10.3389/fncom. 2017. 00103.
[9] Dhanya Parameshwaran and T. C. Thiagarajan, “High Variability Periods in the EEG Distinguish Cognitive Brain States,” Brain Sciences, vol. 13, no. 11, pp. 1528–1528, Oct. 2023, doi: https://doi.org/10.3390/brainsci13111528.
[10] I. G. Campbell, “EEG Recording and Analysis for Sleep Research,” Current Protocols in Neuroscience, vol. 49, no. 1, pp. 10.2.1–10.2.19, Oct. 2009, doi: https://doi.org/10.1002/0471142 301.ns1002s49.
[11] Aravindpai Pai, “CNN vs. RNN vs. ANN – Analyzing 3 Types of Neural Networks,” Analytics Vidhya, Feb. 17, 2020. https://www.analyticsvidhya.com/blog/ 2020/02/ cnn-vs-rnn -vs -mlp -analyzing-3-types-of-neural-networks-in-deep-learning/
[12] IBM, “What is supervised learning? | IBM,” IBM, 2023. https://www.ibm.com/topics/supervised-learning.
[13] “GANs vs. VAEs: What is the best generative AI approach? | TechTarget,” Enterprise AI. https://www.techtarget.com/ searchenterpriseai/feature/GANs-vs-VAEs-What-is-the-best-generative-AI-approach
[14] M. Saeidi et al., “Neural Decoding of EEG Signals with Machine Learning: A Systematic Review,” Brain Sciences, vol. 11, no. 11, p. 1525, Nov. 2021, doi: https://doi.org/10.3390/brainsci 11111525.
[15] “Dream engineering: Simulating worlds through sensory stimulation,” Consciousness and Cognition, vol. 83, p. 102955, Aug. 2020, doi: https://doi.org/10.1016/j.concog.2020.102955.
Received : 28 September 2023
Accepted : 04 December 2023
Published : 09 December 2023
DOI: 10.30726/esij/v10.i4.2023.104002