Affiliations: [a] Department of Power, Electronics and Telecommunications Engineering, Faculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia | [b] Department of Psychology, Faculty of Philosophy, University of Banja Luka, Banja Luka, Republic of Srpska, Bosnia and Herzegovina
Correspondence:
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Corresponding author: Nemanja Gazivoda, Department of Power, Electronics and Telecommunications Engineering, Faculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia. E-mail: nemanjagazivoda@uns.ac.rs.
Abstract: BACKGROUND: Cognitive neuroscience experiments require accurate and traceable methods of measuring cognitive phenomena, analyzing and processing data, and validating results, including measurement of impact of such phenomena on brain activity and consciousness. EEG measurement is the most widely used tool for evaluation of the experiment’s progress. To extract more information from the EEG signal, continuous innovation is necessary to provide a broader range of information. OBJECTIVE: This paper presents a new tool for measuring and mapping cognitive phenomena using time window-based multispectral brain mapping of electroencephalography (EEG) signals. METHODS: The tool was developed using Python programming language and enables users to create brain maps images for six spectra (Delta, Theta, Alpha, Beta, Gamma, and Mu) of EEG signal. The system can accept an arbitrary number of EEG channels with standardized labels based on the 10–20 system, and users can select the channels, frequency bandwidth, type of signal processing, and time window length to perform the mapping. RESULTS: The key advantage of this tool is its ability to perform short-time brain mapping, which allows for the exploration and measurement of cognitive phenomena. The tool’s performance was evaluated through testing on real EEG signals, and results demonstrated its effectiveness in accurately mapping cognitive phenomena. CONCLUSION: The developed tool can be used in various applications, including cognitive neuroscience research and clinical studies. Future work involves optimizing the tool’s performance and expanding its capabilities.
