Artificial nerve cells assist translate cortical signals


neuron Credit: Pixabay/CC0 Public Domain Russian researchers have proposed a brand-new algorithm for automated decoding and interpreting the decoder weights, which can be utilized both in brain-computer interfaces and in basic research. The outcomes of the study were released in the Journal of Neural Engineering.

Brain-computer user interfaces are needed to create robotic prostheses and neuroimplants, rehabilitation simulators, and gadgets that can be managed by the power of idea. These gadgets assist individuals who have suffered a stroke or physical injury to move (when it comes to a robotic chair or prostheses), interact, utilize a computer, and operate home appliances. In addition, in mix with machine learning methods, neural interfaces assist researchers comprehend how the human brain works.

The majority of often brain-computer user interfaces utilize electrical activity of neurons, measured, for instance, with electro- or magnetoencephalography. However, an unique decoder is required in order to equate neuronal signals into commands. Traditional methods of signal processing require painstaking work on identifying helpful features– signal qualities that, from a scientist’s point of view, appear to be most important for the deciphering task.

At first, the authors concentrated on electrocorticography (ECoG) data– an intrusive recording of neural activity with electrodes located directly on the cortical surface under the dura mater, a shell that encapsulates brain– and established a synthetic neural network architecture that automates the extraction of interpretable functions.

As conceived by the scientists, the neural network algorithm must not be too complicated in regards to the variety of specifications. It ought to be automatically tuned and allow one to analyze the learned parameters in physiologically significant terms. The last requirement is specifically important: if it is met, the neural network can be utilized not just to decode signals, but likewise to acquire brand-new insights into the neuronal mechanisms, the dream come true for neuroscientists and neurologists. Therefore, in addition to a brand-new neural network for signal processing, the authors proposed (and theoretically warranted) a technique for analyzing the specifications of the broad class of neural networks.

The neural network proposed by the scientists includes several likewise structured branches, each of which is automatically tuned to evaluate the signals of a separate neural population in a particular frequency variety and is tuned far from interference. To do this, they use convolutional layers comparable to those that consist of neural networks, honed for image analysis, and which function as spatial and frequency filters. Understanding the weights of the spatial filter, it is possible to determine where the neural population lies, and the temporal convolution weights show how the nerve cell activity modifications over time in addition to indirectly showing the neuronal population size.

To examine the performance of their neural network in combination with a brand-new approach for translating its specifications, the researchers initially generated a set of realistic model information, or 20 minutes of activity from 44 neuron populations. Noise was added to the data to mimic disturbance when taping signals in real conditions. The second set of information to inspect was a dataset from the BCI Competition IV, including the ECoG information of a number of subjects who periodically moved their fingers spontaneously. Another set of ECoG data was gathered by the scientists themselves at the Moscow State University of Medication and Dentistry, which works as the clinical base of the Centre for Bioelectric Interfaces of HSE University. Unlike previous information, the records gathered by the scientists consisted of complete geometric details about the location of the ECoG electrodes on the surface area of each client’s cortex. This made it possible to interpret the weights of the spatial filters found out by the neural network and to recognize somatotopy (i.e., the relationship between the neural population’s position on the cortex and the body part it functionally corresponds to) in the place of the nerve cell populations essential for decoding the motion of each finger.

The neural network carried out perfectly: with the BCI Competitors IV dataset, it worked on par with the solution proposed by the competition winners, however, unlike the option, it used immediately chosen functions. While dealing with both genuine and model information, the researchers proved that it is possible to interpret the scale specifications correctly and in detail, and the interpretation offers physiologically possible results. The scientists also applied a new strategy to the classification of fictional motions based on non-invasive (obtained from the surface area of the head, without implanting electrodes) EEG information. As when it comes to ECoG, the neural network supplied high decoding precision and function interpretability.

“We are currently using this technique to develop intrusive brain-computer user interfaces, in addition to solve issues of preoperative cortex mapping, which is required to guarantee that essential behavioral functions are maintained after brain surgical treatment,” says the scientific lead of the research study and the director of the HSE Center for Bioelectric Interfaces, Alexei Ossadcthi. “In the closest future, the industrialized strategy will be used to instantly draw out understanding about the concepts according to which the brain implements a broad series of behavioral functions.”

New method discovered for energy-efficient AI applications More details: Artur Petrosyan et al, Decoding and analyzing cortical signals with a compact convolutional neural network, Journal of Neural Engineering (2021 ). DOI: 10.1088/ 1741-2552/ abe20e

Offered by National Research University Higher School of Economics

Citation: Artificial neurons assist decipher cortical signals (2021, March 23) obtained 23 March 2021 from

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