The future of brain electronics and neural interfaces

By Jacob Walker

In an ever-evolving era of technological innovation, the field of brain electronics has undergone rapid advancement and growth. The development of brain-computer interfaces and the progress made in applications of this technology have the potential to completely revolutionize the way in which humans interact with computers and to counter a plethora of neurological disorders and neuro-physiological limitations.

In order to record neural signals, the implanted device must be biocompatible and must not compromise nor damage the surrounding neural network or cerebral tissue. The implanted device should comprise of uncharged, inert materials which will not interfere with essential brain chemistry. Due to brain plasticity, neural networks are able to reorganize themselves following device implementation, allowing for more invasive implants. Invasive implants are neurosurgically inserted protrude deeper into the cerebrum and can detect more meaningful brainwaves. This involves placing the implants on the cortical surface or in the cortex.

Attempting to design a functional and chemically inert neural interface can be problematic as there are several limitations and holdbacks. One holdback is that once implanted, the device cannot be moved to another region of the brain. This is because of the brains cortico-plasticity – that is, once the device has been implanted the neurons rearrange themselves to accommodate it. So, if the device is moved there is an empty intracortical cavity. Another limitation is the inability to record data signals from large numbers of neurons. This is because there is a lot of background noise and interference that is recorded. Background noise could be caused by anything – for example, the opening and closing of an eyelid. There is no useful signal output that can be detected. In order to improve the signal to noise ratio, signals need to be preprocessed. Independent component analysis is a technique which is used for spatial filtering. This technique involves decomposing the original signal into its constituents and selecting only the meaningful signals while disregarding the others. (Sarah N. Abdulkader et al., 2015).

Current pioneers in brain electronics include most prominently, Neuralink, among others. Neuralink has enjoyed good progress on the development of their neural interface. The approach taken by Neuralink is very innovative. They designed a surgical robot which inserts thin filaments, called neural lace, into the cortex of the cerebrum (Elon Musk, 2019). There are thousands of these channels which are connected to a custom sensor which receives neural signals and decrypts them for analysis. They aim to incorporate full Bluetooth connectivity with the implanted device which allows for cross-connection with a handheld device.

There is a lot of space for innovation with brain-computer interfaces. Medical applications of brain electronics include restoration of cognitive, sensory, and motor functions, rehabilitation against neurodegenerative conditions, and preventative care, among others.

The restoration of motor function is a viable application of neural interfaces. The neural interface can relay electric signals to a mechanically prosthetic limb so as to control the limb. For this to be operable, the prosthetic limb must be biocompatible and must be able to decipher the electric signals it is receiving from the interface. 

There are also considerable research efforts investigating the use of brain electronics to solve neurological problems and neurodegenerative diseases, for instance, Parkinson’s disease. The implantable device can be configured so that it delivers electrical impulses deep within the cortex, stimulating these neurons, which is therapeutically helpful for Parkinson’s patients. Some researchers have begun to use neural signals to detect idiopathic rapid eye movement sleep behaviour disorder (iRBD) as this usually a strong indicator for early stage Parkinson’s. (Sarah N. Abdulkader et al., 2015). Brain-computer interfaces can also be used to help with Alzheimer’s patients. They can potentially restore neural connections in the impaired area of the brain which can be therapeutic for the patient (Mak and Wolpaw, 2009).

Another interesting application would be preventative care. Brain electronics could be utilized so as to prevent dangerous substance addictions like cocaine, alcohol, or nicotine. The implant could provide electrical signals which stimulate certain sections of the brain, causing the need for a certain substance to subside. Another similar application involves the use of neural interfaces to identify signs of epileptic seizures and brain tumours. Sharanreddy and Kulkarni used a unique method to decompose the acquired signal using a multi-wavelet transform, whereafter they were able to identify if the sub-signal represents epilepsy or some other neurological condition (Sharanreddy and Kulkarni, 2013).

The future of brain electronics is exciting and full of promise. That being said, there is still a lot of room for amelioration and development in the field before applications such as neuroprosthetic control and perhaps even brain-to-brain communications can be wholly achieved. 

References:

Hughes, Mark A. “Insinuating Electronics in the Brain.” The Surgeon : Journal of the Royal Colleges of Surgeons of Edinburgh and Ireland, Publications Office, The Royal College of Surgeons of Edinburgh, Aug. 2016, http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5122671/. 

Abdulkader, Sarah N., et al. “Brain Computer Interfacing: Applications and Challenges.” Egyptian Informatics Journal, Elsevier, 6 July 2015, http://www.sciencedirect.com/science/article/pii/S1110866515000237. 

Sharanreddy and Kulkarni “Automated EEG Signal Analysis for Identification of Epilepsy Seizures and Brain Tumour.” Taylor & Francis, http://www.tandfonline.com/doi/abs/10.3109/03091902.2013.837530. 

Mak, Joseph N, and Jonathan R Wolpaw. “Clinical Applications of Brain-Computer Interfaces: Current State and Future Prospects.” IEEE Reviews in Biomedical Engineering, U.S. National Library of Medicine, 2009, http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2862632/. 

Neuralink ; Musk, Elon, et al. “An Integrated Brain-Machine Interface Platform with Thousands of Channels.” Journal of Medical Internet Research, JMIR Publications Inc., Toronto, Canada, 2016 http://www.jmir.org/2019/10/e16194/. 

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