Akademik Veri Yönetim Sistemi
ICENS2019, 12 - 16 Haziran 2019
In this study, methods related to amplitude
thresholding that are one of the processing steps of Brain Machine Interface
(BMI) applications are compiled. The BMI measures the change in the activity of
brain cells to any stimuli. Then, it can also extract behavioral data
corresponding to this measurement without behavioral data. The running of the
BMI can be summarized as follows: Firstly, the neural activity record is
filtered interval a band of included of action potential (AP). The AP
candidates contained in the filtered record are determined by amplitude
thresholding method. Suprathreshold waveforms are classified and the AP
sequences that are generated by individual neurons are determined. AP sequences
model as a function of behavioral variables, so it is found out what kind of
information is encoded in the activity record. By using the activity models in
the decoders, the behavioral data are extracted from the activity record. These
processes are speed-limiting due to the pattern recognition and classifying
that must be performed on hundreds of electrodes. Whether or not password
solution can be performed without these processes is trend research topic at
last few years. The optimization of the amplitude threshold is one of the most
important subheadings of this topic. In literature, the amplitude threshold is
calculated as three to five times the standard deviation of the signal. Another
method commonly used to calculate amplitude thresholds is to set a threshold
between three and five times the RMS voltage of the signal. So, the amplitude
threshold should be calculated as user-independent, faster and data-driven. To
overcome these disadvantages, an approach based on the modeling of the
probability distribution of the signal, called the Truncation Thresholds, has
been proposed and proved to be superior to other methods in the literature.
Thus, contribution to literature has been provided for BMI.