The Neurotechnology and Bioinformatics Laboratory operating within our university is an advanced research and application center located at the intersection of neuroscience and bioinformatics, where biological signals are processed and analyzed using engineering and computer science methods.
What Is the Neurotechnology and Bioinformatics Laboratory? / What Is Its Purpose?
Main Purpose: The main purpose of the Neurotechnology and Bioinformatics Laboratory is to provide students with a direct and practical experience environment in scientific studies to be conducted in the field of neuroscience. The laboratory aims to make data obtained from complex biological and neurological systems meaningful using modern engineering methods and to solve problems in different disciplines through technical algorithms.
Devices and Technical Equipment Available in the Laboratory
Key Features: The laboratory infrastructure is configured with specialized software and hardware development kits to enable high-accuracy execution of brain-computer interface (BCI), neurological data acquisition, and bioinformatics modeling processes.
EEG Emotiv Epoc Neuroheadset SDK: A multi-channel neuro-headset system equipped with a software development kit (SDK) that wirelessly records the brain's bio-electric activity, enables the collection of raw EEG (Electroencephalography) data, and provides real-time signal transmission.
Signal Processing and Data Analysis Units: High-performance computing infrastructure that performs noise removal, frequency filtering, and statistical analysis processes prior to classification of the collected raw data.
Research and Application Areas
Academic Contribution: Projects and practical training conducted in the laboratory are based on processing raw data obtained from the nervous system using artificial intelligence and engineering-based algorithms. The laboratory's main research and application areas are as follows:
Raw EEG Data Collection and Processing: Obtaining real-time and high-accuracy neurological data from participants or experimental processes using the EEG Emotiv Epoc system.
Signal Noise Removal (Artifact Removal): Cleaning noise in the collected raw EEG data caused by muscle movements, eye blinks, or environmental electromagnetic factors through advanced filtering steps.
Feature Extraction Methods (Feature Extraction): Revealing the distinguishing characteristics of the signal through mathematical methods in order to make the cleaned signal data meaningful and prepare it for the classification stage.
Engineering Methods and Optimization Algorithms: Testing modern optimization algorithms to maximize signal classification performance and applying them to complex problems in different disciplines.
Contributions to Students and Industry
Practical Application Opportunity: Students reinforce the abstract concepts they learn in theoretical neuroscience and bioinformatics courses by practicing directly with devices at industrial and academic standards, such as the EEG Emotiv Epoc Neuroheadset SDK.
Interdisciplinary Problem-Solving Competence: Students who use engineering, computer science, and neuroscience methods together gain a perspective on solving complex problems in different disciplines through analytical methods.
Algorithm Development and Performance Optimization: Researchers working on optimization algorithms aimed at improving classification performance gain proficiency in the infrastructure of next-generation artificial intelligence and machine learning models.
