All ETDs from UAB

Advisory Committee Chair

Abidin Yildirim

Advisory Committee Members

Arie Nakhmani

Franklin Amthor

Leon Jololian

Document Type

Thesis

Date of Award

2019

Degree Name by School

Master of Science in Electrical Engineering (MSEE) School of Engineering

Abstract

The detection of neural spikes in real-time and accurately has become the center of interest for the researchers in the field of brain-machine/computer interface (BMI/BCI). The primary challenge in the Brain-Machine interface is to translate raw neuronal response signals into the control of electrical actuators. Only accurate and rapid classification of neural response can help efficiently and conveniently to disable peoples, particularly those suffering from spinal cord injury, stocks, etc., Recording from neurons and analyzing them with many different methods are not new. However, the primary challenge here is the real-time recording and classifying the spikes with higher accuracy. In particular, the last, i.e., accurate detecting and classifying the neural response is very paramount requirements for an efficient and functional BMI/BCI. In this approach, the slope values are found for each sample for creating spikes/non-spike templates. These templates are formed with a specific slope sequence and are fed into a fully connected Neural Network for classification. To evaluate this algorithm, two approaches were tested which are shape-based spike extraction and threshold-based spike extraction. Both approaches outperform in terms of efficiency and precision. Moreover, it also proves the methods for extracting a spike template without any known features of the spike.

Included in

Engineering Commons

Share

COinS