Amplitude Thresholding Methods in Neural Recordings:A Review

In this study, methods related to amplitudethresholding that are one of the processing steps of Brain Machine Interface(BMI) applications are compiled. The BMI measures the change in the activity ofbrain cells to any stimuli. Then, it can also extract behavioral datacorresponding to this measurement without behavioral data. The running of theBMI can be summarized as follows: Firstly, the neural activity record isfiltered interval a band of included of action potential (AP). The APcandidates contained in the filtered record are determined by amplitudethresholding method. Suprathreshold waveforms are classified and the APsequences that are generated by individual neurons are determined. AP sequencesmodel as a function of behavioral variables, so it is found out what kind ofinformation is encoded in the activity record. By using the activity models inthe decoders, the behavioral data are extracted from the activity record. Theseprocesses are speed-limiting due to the pattern recognition and classifyingthat must be performed on hundreds of electrodes. Whether or not passwordsolution can be performed without these processes is trend research topic atlast few years. The optimization of the amplitude threshold is one of the mostimportant subheadings of this topic. In literature, the amplitude threshold iscalculated as three to five times the standard deviation of the signal. Anothermethod commonly used to calculate amplitude thresholds is to set a thresholdbetween three and five times the RMS voltage of the signal. So, the amplitudethreshold should be calculated as user-independent, faster and data-driven. Toovercome these disadvantages, an approach based on the modeling of theprobability distribution of the signal, called the Truncation Thresholds, hasbeen proposed and proved to be superior to other methods in the literature.Thus, contribution to literature has been provided for BMI.