A Comprehensive Study on Smart Brain Chip Technologies for Precision Mental Health Monitoring and Treatment

Authors

  • Viroja Yeshpatel
  • Ashutosh Solanki
  • Nihar Mehta
  • Atodariya Rushikumar Dilavarsinh
  • Vasoya Divyaben Ashokbhai
  • Vishwa Alpeshbhai Thakkar
  • Vivek Dave

Keywords:

Smart Brain Chips, Neural Interfaces, AI in Mental Health, Neurotechnology, Precision Medicine, Brain-Computer Interfaces

Abstract

Smart brain-chip technologies have improved both the accuracy and time for diagnostic and treatment of mental health. This article offers an in-depth look at neural interfaces that have been developed, used, and have had practical implications in the treatment of mental disorders via real-time cognitive function simulation. Thanks to multidisciplinary merging of neuroimaging, the most advanced machine learning as well as engineering, these devices will make room for the development of new therapies useful for e.g. depression, anxiety, bipolar disorder, and PTSD. The article points to the fact that intelligent brain chips are able to carry out very precise diagnostics and also allow fast treatment, which, in turn, can lead to positive outcomes in patients. Nevertheless, there are still some issues that need to be answered such as the infringement of data privacy, the risks of ethical nature, and the effects of neural interventions in the future that are not yet well-known. The study deals with these problems by connecting them to issues found in the application of smart brain chips in mental health care.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

References

Wolpaw, J. R., et al., "Brain-computer interfaces for communication and control," Clinical Neurophysiology, vol. 113, no. 6, pp. 767–791, 2002.

Nicolelis, M. A. L., "Actions from thoughts," Nature, vol. 409, no. 6818, pp. 403–407, 2001.

Lebedev, M. A., & Nicolelis, M. A. L., "Brain-machine interfaces: Past, present, and future," Trends in Neurosciences, vol. 29, no. 9, pp. 536–546, 2006.

Schalk, G., et al., "BCI2000: A general-purpose brain-computer interface (BCI) system," IEEE Trans. Biomed. Eng., vol. 51, no. 6, pp. 1034–1043, 2004.

Hochberg, L. R., et al., "Neuronal ensemble control of prosthetic devices by a human with tetraplegia," Nature, vol. 442, no. 7099, pp. 164–171, 2006.

Bishop, C. M., Neural Networks for Pattern Recognition, Oxford Univ. Press, 1995.

LeCun, Y., et al., "Gradient-based learning applied to document recognition," Proc. IEEE, vol. 86, no. 11, pp. 2278–2324, 1998.

Vapnik, V., Statistical Learning Theory, Wiley, 1998.

Bengio, Y., et al., "A neural probabilistic language model," J. Mach. Learn. Res., vol. 3, pp. 1137–1155, 2003.

Hinton, G. E., & Salakhutdinov, R. R., "Reducing the dimensionality of data with neural networks," Science, vol. 313, no. 5786, pp. 504–507, 2006.

Benabid, A. L., et al., "Combined (thalamotomy and stimulation) stereotactic surgery of the VIM thalamic nucleus for bilateral Parkinson's disease," Stereotactic and Functional Neurosurgery, vol. 50, pp. 344–346, 1987.

Mayberg, H. S., et al., "Deep brain stimulation for treatment-resistant depression," Neuron, vol. 45, no. 5, pp. 651–660, 2005.

Polikov, V. S., Tresco, P. A., & Reichert, W. M., "Response of brain tissue to chronically implanted neural Electrodes," J. Neuroscience Methods, vol. 148, no. 1, pp. 1–18, 2005.

Schwartz, A. B., "Cortical neural prosthetics," Annu. Rev. Neuroscience, vol. 27, pp. 487–507, 2004.

Donoghue, J. P., "Connecting cortex to machines: Recent advances in brain interfaces," Nat. Neurosci., vol. 5, suppl. 11, pp. 1085–1088, 2002.

Illes, J., & Bird, S. J., "Neuroethics: A modern context for ethics in neuroscience," Trends in Neurosciences, vol. 29, no. 9, pp. 511–517, 2006.

Farah, M. J., "Emerging ethical issues in neuroscience," Nat. Neurosci., vol. 5, no. 11, pp. 1123–1129, 2002.

Clausen, J., "Moving minds: Ethical aspects of neural motor prostheses," Biotechnology Journal, vol. 3, no. 12, pp. 1493–1501, 2008.

Ienca, M., & Haselager, P., "Hacking the brain: Brain-computer interfacing technology and the ethics of neurosecurity," Ethics Inf. Technol., vol. 18, no. 2, pp. 117–129, 2016.

Greely, H. T., "Regulating human biological enhancements: Questionable justifications and international complications," The Mind, the Body, and the Law, pp. 1–26, 2006.

Sanei, S., & Chambers, J. A., EEG Signal Processing, Wiley, 2007.

Mead, C., Analogue VLSI and Neural Systems, Addison-Wesley, 1989.

Brown, E. N., Kass, R. E., & Mitra, P. P., "Multiple neural spike train data analysis: State-of-the-art and challenges," Nat. Neurosci., vol. 7, no. 5, pp. 456–461, 2004.

Miranda, E. R., et al., "Brain-computer music interfacing: From basic research to the real world," Front. Hum. Neurosci., vol. 9, art. 129, 2015.

Kübler, A., et al., "Brain-computer communication: Unlocking the locked-in," Psychol. Bull., vol. 127, no. 3, pp. 358–375, 2001.

Perlmutter, J. S., & Mink, J. W., "Deep brain stimulation," Annu. Rev. Neuroscience, vol. 29, pp. 229–257, 2006.

Nuttin, B., et al., "Electrical stimulation in anterior limbs of internal capsules in patients with obsessive-compulsive disorder," Lancet, vol. 354, no. 9189, p. 1526, 1999.

George, M. S., et al., "A controlled trial of daily left prefrontal cortex TMS for treating depression," Biol. Psychiatry, vol. 48, no. 10, pp. 962–970, 2000.

Fregni, F., & Pascual-Leone, A., "Noninvasive brain stimulation in neurology—therapeutic potential of rTMS," Nat. Clin. Pract. Neurol., vol. 3, no. 7, pp. 383–393, 2007.

Rosin, B., et al., "Closed-loop deep brain stimulation is superior in ameliorating Parkinsonism," Neuron, vol. 72, no. 2, pp. 370–384, 2011.

Downloads

Published

2025-05-15

How to Cite

1.
Yeshpatel V, Solanki A, Mehta N, Dilavarsinh AR, Ashokbhai VD, Thakkar VA, Dave V. A Comprehensive Study on Smart Brain Chip Technologies for Precision Mental Health Monitoring and Treatment. J Neonatal Surg [Internet]. 2025May15 [cited 2025Oct.4];14(23S):1034-42. Available from: https://jneonatalsurg.com/index.php/jns/article/view/5883

Issue

Vol. 14 No. 23S (2025): Journal of Neonatal Surgery

Section

Original Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

You are free to:

  • Adapt — remix, transform, and build upon the material for any purpose, even commercially.

Terms:

  • Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
  • No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.