How EEG-Interpreting Software Works

Artificial Intelligence

Artificial intelligence (AI) can be defined as technology that mimics and even improves upon human intelligence (Wang & Preininger, 2019).

Artificial intelligence has been applied to analysis of various types of human health data. Machine learning and deep learning both fall under the broader category of artificial intelligence.

However, levels of complexity differentiate the two technologies. Machine learning involves more basic functions, while deep learning is a more complex form of machine learning that creates models from the available data (Wang & Preininger, 2019).


Machine Learning

A review of the extant literature consistently demonstrates that one of the more accurate means of interpreting electroencephalogram (EEG) data appears to be the support vector machine (SVM) model, which is a type of machine learning (e.g., Li et al., 2019; Mahato & Paul, 2020; Mumtaz et al., 2017). Of note, research that examined methods of EEG analysis

analysis between 1988 and 2018 corroborated this finding. This study found that virtually all types of
machine learning models have been utilized to interpret EEGs, and further, supervised learning methods
such as SVM and k-nearest-neighbors (KNN) were most accurate (Hosseini et al., 2021).

Challenges to Model Development

Support vector machine model

Offers greater diagnostic accuracy

Her main problem:

Offers greater diagnostic accuracy

Utilizing deep learning:

Way to minimize the amount of human intervention needed to analyze data

Unlike other types of health-related data, interpreting EEGs presents unique challenges due to the continuous nature of brain waves as well as the large amount of data generated.

Therefore, additional steps are required before accurate interpretation can occur, including denoising and calibration. Another concern in developing accurate predictive models involves measurement error from physical devices.

Contributors to noise on EEGs include equipment interference, motor movements and eye movements. Among the mother wavelet functions tested, coif3 was found to be most effective in addressing these types of noise (Alkareem Alyasseri, 2017).

Computer-Aided Diagnosis

Use of artificial intelligence to develop a second opinion for the purpose of assisting clinicians in diagnostic decision-making.


The depression diagnosis index

Category of automated computer diagnosis that assists clinicians in using EEGs to diagnose depression.

The depression diagnosis index proposed by Archarya and colleagues combines multiple nonlinear features into one numerical score that translates to an objective diagnosis (Archarya et al., 2015a). However, due to the current state of development of EEG technology, the depression diagnosis index may also be considered computer-aided diagnosis, as human clinical judgment may be necessary to fully describe depressive disorder features and specifiers.

Neuroscience Software

The Role of Neuroscience for Software

Without the use of computer software, EEGs are difficult to interpret. Computer–aided diagnosis (CAD) facilitates integration of both linear and nonlinear methods of interpreting EEGs into initial detection of depression as well as confirming human diagnostic assessments.

In addition to improving patient quality of life, the end goal of this product is ultimately to significantly reduce suicide as one of the outcomes of depression.

Neuroscience Software aims to provide a user-friendly

technical solution to facilitate the ability of clinicians to facilitate the ability of clinicians to diagnose accurately and provide timely treatment, as well as monitor response to treatment. This will be accomplished through analysis of both linear and non-linear data.

The software will integrate with a number of existing EEG measurement devices to analyze biomarkers and provide a depression diagnosis index on a scale ranging from 1 to 10, in which 1 indicates low probability of depression and 10 indicates high probability.

Referenses learn more

Archarya, U. R., Sudarshan, V. K., Adeli, H., Santhosh, J., Koh, J. E., Puthankatti, S. D., & Adeli, A. (2015a). A novel depression diagnosis index using nonlinear features in EEG signals. European Neurology, 74(1-2), 79-83. doi:10.1159/000438457
Acharya, U. R., Sudarshan, V. K., Adeli, H., Santhosh, J., Koh, J. E., & Adeli, A. (2015b). Computer-aided diagnosis of depression using EEG signals. European Neurology, 7(3), 329-336. doi:10.1159/000381950
Alkareem Alyasseri, Z. A. (2017). Electroencephalogram signals denoising using various mother wavelet functions: A comparative analysis. Proceedings of the International Conference on Imaging, Signal Processing and Communication, 100-105. doi:10.1145/3132300.3132313
Doi, K. (2007). Computer-aided diagnosis in medical imaging: Historical review, current status and future potential. Computerized Medical Imaging and Graphics, 31(4-5), 198-211. doi:10.1016%2Fj.compmedimag.2007.02.002
Hosseini, M. P., Hosseini, A., & Ahi, K. (2021). A review on machine learning for EEG signal processing in bioengineering. IEEE Reviews in Biomedical Engineering, 14, 204-218. doi:10.1109/RBME.2020.2969915
Li, Y., Hu, B., Zheng, X., & Li, X. (2019). EEG-based mild depressive disorder detection using differential evolution. IEEE Access, 7, 7814-7822. doi:10.1109/ACCESS.2018.2883480.
Mahato, S., & Paul, S. (2019). Electroencephalogram (EEG) signal analysis for diagnosis of major depressive disorder (MDD): A review. In Nath, V., & Mandal, J. K., (Eds.), Nanoelectronics, Circuits and Communication Systems (pp. 323-335). Springer.
Mumtaz, W., Xia, L., Azhar Ali, S. S., Mohd Yasin, M. A., Hussain, M., & Saeed Malik, A. (2017). Electroencephalogram (EEG)-based computer-aided technique to diagnose major depressive disorder (MDD). Biomedical Signal Processing and Control, 31, 108-115. doi:10.1016/j.bspc.2016.07.006
Wang, F., & Preininger, A. (2019). AI in health: Sate of the art, challenges, and future directions. Yearbook of Medical Informatics, 28(1), 16-26. doi:10.1055%2Fs-0039-1677908