7
Introductory Chapter: Methods and Applications of Neural Signal Processing
DOI: http://dx.doi.org/10.5772/intechopen.93335
Author details
Dingyi Pei and Ramana Vinjamuri*
Department of Biomedical Engineering, Stevens Institute of Technology,
United States
*Address all correspondence to: ramana.vinjamuri@gmail.com
efficient algorithms. The adaptability and reliability over the long-term are current
challenges that are being addressed using advanced and adaptive signal processing methodologies. Months to years of training are essential to operate prosthetic
or exoskeleton skillfully. This training time can possibly be reduced by increasing
the burden on machine learning algorithms that are currently being addressed by
advanced signal processing methods.
4.3 Neurological disorders
Epilepsy is a common neurological disorder characterized by an enduring predisposition to generate epileptic seizures [15]. These seizures may cause
disturbances in movement, loss of control of bowel or bladder function, loss of
consciousness, or other disturbances in cognitive functions. Currently, the signal
processing algorithms can detect ongoing seizures and provide clinicians with
detailed information such as localization of seizure foci useful for the treatment
of epilepsy. The ability to detect seizures rapidly and accurately could promote
therapies aimed at rapid treatment of seizures.
Skilled neurophysiologists visually examine the neural signals and detect epilepsy.
Apart from the single-channel signals, other contextual information such as spatial
and temporal data are vital to neurophysiologists for recognizing spikes [16, 17].
Currently, the epileptic seizures can be detected and predicted from EEG or ECoG signals by extracting the hidden features using machine learning algorithms [15, 18–20].
5. Conclusion
Neural signal processing has become an increasingly important tool in neuroscience and neural engineering. This chapter provides a general overview of the widely
implemented neural recording and stimulation technologies, neural processing
methodologies, and how these techniques can be practically executed in some practical applications. The understanding of the neural representations of human behavior
in the brain can be significantly enhanced using neural signal processing methodologies. With the advances in neural recording techniques and parallel advances in neural signal processing, we believe that several unanswered questions and challenges in
neuroscience and neural engineering will be solved in the near future.
© 2020 The Author(s). Licensee IntechOpen. Distributed under the terms of the Creative
Commons Attribution - NonCommercial 4.0 License (https://creativecommons.org/
licenses/by-nc/4.0/), which permits use, distribution and reproduction for
non-commercial purposes, provided the original is properly cited.
Introductory Chapter: Methods and Applications of Neural Signal Processing
DOI: http://dx.doi.org/10.5772/intechopen.93335
Author details
Dingyi Pei and Ramana Vinjamuri*
Department of Biomedical Engineering, Stevens Institute of Technology,
United States
*Address all correspondence to: ramana.vinjamuri@gmail.com
efficient algorithms. The adaptability and reliability over the long-term are current
challenges that are being addressed using advanced and adaptive signal processing methodologies. Months to years of training are essential to operate prosthetic
or exoskeleton skillfully. This training time can possibly be reduced by increasing
the burden on machine learning algorithms that are currently being addressed by
advanced signal processing methods.
4.3 Neurological disorders
Epilepsy is a common neurological disorder characterized by an enduring predisposition to generate epileptic seizures [15]. These seizures may cause
disturbances in movement, loss of control of bowel or bladder function, loss of
consciousness, or other disturbances in cognitive functions. Currently, the signal
processing algorithms can detect ongoing seizures and provide clinicians with
detailed information such as localization of seizure foci useful for the treatment
of epilepsy. The ability to detect seizures rapidly and accurately could promote
therapies aimed at rapid treatment of seizures.
Skilled neurophysiologists visually examine the neural signals and detect epilepsy.
Apart from the single-channel signals, other contextual information such as spatial
and temporal data are vital to neurophysiologists for recognizing spikes [16, 17].
Currently, the epileptic seizures can be detected and predicted from EEG or ECoG signals by extracting the hidden features using machine learning algorithms [15, 18–20].
5. Conclusion
Neural signal processing has become an increasingly important tool in neuroscience and neural engineering. This chapter provides a general overview of the widely
implemented neural recording and stimulation technologies, neural processing
methodologies, and how these techniques can be practically executed in some practical applications. The understanding of the neural representations of human behavior
in the brain can be significantly enhanced using neural signal processing methodologies. With the advances in neural recording techniques and parallel advances in neural signal processing, we believe that several unanswered questions and challenges in
neuroscience and neural engineering will be solved in the near future.
© 2020 The Author(s). Licensee IntechOpen. Distributed under the terms of the Creative
Commons Attribution - NonCommercial 4.0 License (https://creativecommons.org/
licenses/by-nc/4.0/), which permits use, distribution and reproduction for
non-commercial purposes, provided the original is properly cited.