Neuro-Muscular Engineering
Tahmineh Sadati; Mohammad Reza Daliri
Volume 12, Issue 1 , June 2018, , Pages 1-10
Abstract
A brain-computer interface is a system which works based on the neural activity created by the brain and it has attracted the attention of many researchers in recent years. These interfaces are independent of the usual pathway of peripheral and muscular nerves and are very important because of their ...
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A brain-computer interface is a system which works based on the neural activity created by the brain and it has attracted the attention of many researchers in recent years. These interfaces are independent of the usual pathway of peripheral and muscular nerves and are very important because of their ability to provide a new dimension in communication or control of a device for the disabled persons. The neural activity used in the brain-computer interface can be recorded by various invasive and non-invasive methods and can be converted to the desired signal by different decoding algorithms. In this study, 3 rats were used to perform a movement task which was pressing a key and receiving a drop of water by a mechanical arm for corrected trials. By implanting a 16-channel microelectrode array in the rat's motor cortex during an invasive process, the brain signals are recorded during the task, and simultaneously the signal received by the force sensor is also stored. By performing the necessary preprocessing on spikes and extracting the firing rates of signal as a feature vector by convolving a Gaussian window with the spike trains, the necessary inputs for the decoding algorithm, which is linear regression here, are obtained. Two patterns have been used for cross validation. The first pattern considers 60% of the data from the beginning of the signal as a train set and the remaining 40% of the signal as a test set and the second pattern is the opposite of the first one. Several methods have been used to evaluate the decoding algorithm used in the studies. In this paper, the correlation coefficient and coefficient of determination have been used. The correlation coefficient and coefficient of determination between the desired force and predicted force in linear resgression method, in average of three sessions for three rats, are equal to r=0.56 and =0.20 for the first pattern and r=0.55 and =0.30 for the second pattern respectively. These results show that firing rates of neurons are proper features to predict continous variables such as force. Besides, it can be concluded that linear regression is a suitable method for decoding a motor variable like force and follows the desired signal properly.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Adib Keikhosravi; Edmond Zahedi
Volume 6, Issue 4 , June 2012, , Pages 307-317
Abstract
The photoplethysmogram (PPG) is a low cost and ubiquitous signal and has always had a great significance in cardiovascular parameter identification such as arterial dilation due to a stimulus. The PPG is generally recorded from the fingertip which is affected by the auto-regulation mechanism (ARM), preventing ...
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The photoplethysmogram (PPG) is a low cost and ubiquitous signal and has always had a great significance in cardiovascular parameter identification such as arterial dilation due to a stimulus. The PPG is generally recorded from the fingertip which is affected by the auto-regulation mechanism (ARM), preventing the results to be well correlated with standard methods based on imaging the brachial or radial artery. Based on the fact that the ARM has no effect on conduit arteries, the correlation between fingertip and radial artery PPG is investigated in this work. A custom made probe is fabricated using an array of photodiodes and a 960 nm LED for recording the wrist photoplethysmogram (PPG). The design is based on Monte-Carlo simulation of light propagation in tissues. Two series of experiments were carried-out: normal breathing and deep breathing. In both experiments, index finger and wrist PPG were simultaneously recorded. In the first series of experiments, signals from 9 subjects were recorded and the correlation coefficient for the raw signals (AC+DC), the AC and DC components of wrist and finger PPG were 62.5% ± 12.1%, 91.2% ± 6.9% and 61% ± 13.4% respectively. In the second series of experiment (deep breathing), signals from 6 subjects were recorded and the correlation coefficient for the raw signals (AC+DC), the AC and DC components were 89.7% ± 5.9%, 93.7% ± 3.3% and 89.9% ± 5.9% respectively. These results show that under normal breathing conditions, only the AC components of the PPG signals are highly correlated. When respiration becomes the dominant effect, both AC and DC parts become highly correlated.
