EEG Data Modelling
The Brain-Like Artificial Intelligence (BLAI) is pioneered by Prof.Nikola Kasabov and here it is applied to a specific application.
Some of the applications of this system have been developed thorough a collaboration between KEDRI and other international and national institutes such as the Faculty of Health and Environmental Science of AUT and the Neulab of the Mediterranean University of Reggio Calabria.
EEG records brain signals through electrodes on the scalp and is widely used in research, as it is non-invasive and has good temporal and spatial resolution. However, EEG systems have been criticized, because of the time consuming and complex training period for the potential user. Nevertheless, EEG is the most commonly collected data for the study of brain processes.
Recently, a growing number of tools have been developed to process this data. Classification of EEG signals is often done with standard statistical and artificial intelligence methods. All these techniques often require extensive preprocessing of the data and also signal averaging. More efficient techniques are required, as the EEG can provide powerful information that, if understood, can be used to stage and diagnose cognitive impairments, predict neurological disorders and dementia, help developing device that can support the neurorehabilitation process, etc.
In this respect, a major contribution to research can be brought about by a much more efficient methodology based on machine learning techniques that can be used to evaluate the EEG signals of a person with a deeper understanding of the underlying brain processes.
Here, we have developed a new spiking neural network methodology and a system for the classification and analysis of EEG data. This system can be used to model, recognise and understand complex EEG data recorded during different scenarios. The approach used in this research is based on the spiking neural networks NeuCube architecture.
The NeuCube-based approach leads to faster data processing, improved accuracy of the EEG data classification and improved understanding of the brain information and the cognitive processes that generated it.
All of this proves that the new proposed methodology and system can constitute an important contribution to the IS field of research, as it allows for many applications to be developed, such as: new types of BCI; early detection of cognitive decline to be used by clinicians in everyday diagnosis; neuromarketing based on EEG data; pain detection; cognitive games; and many more.
FIGURE. The image shows the NeuCube architecture for EEG data classification and knowledge extraction. The picture shows the NeuCube three principal sub-modules: the input encoding sub-module, where EEG data is encoded into trains of spikes that are then presented to the main sub-module, the SNN cube; the NeuCube main sub-module, where time and space characteristics of the data are captured and learned; and the output sub-module for data classification and new knowledge discovery from the SNNc visualization. Image from (Kasabov, 2014a).
Related Papers and Benchmarking
The proposed methods and systems, when compared with traditional statistical and machine learning methods, as demonstrated to offer several avantages, such as:
- No need of preprocessing of the data (such as normalization, scaling, smoothing, etc.);
- Fast learning of EEG data with one iteration data propagation only;
- Better classification accuracy (see table below from Kasabov & Capecci, 2014);
- Ability to adapt to new data and new classes through incremental learning;
- Connectivity analysis of the model after unsupervised training of EEG data from different groups of subjects/classes to extract new knowledge and to study the brain regions involved;
- Connectivity analysis for monitoring the treatment of patients and the state of their cognitive brain activity;
- Classification and connectivity analysis for predicting patient response to treatment;
- The model capability for use in brain-like mapping, to study the specific characteristics of neurological events, revealing the area of the brain where it occurs. Leading eventually to future prediction of neurological events based on the initial information;
- The model capability for use in neuromorphic learning, for diagnostic decision support systems, personalised prognosis, and early prediction of a neurological event;
See also some of the related papers:
Kasabov, N., & Capecci, E. (2015). Spiking neural network methodology for modelling, classification and understanding of EEG spatio-temporal data measuring cognitive processes. Information Sciences, 294, 565-575.
Capecci, E., Wang, G., Kasabov, N. (2015). Analysis of connectivity in a NeuCube spiking neural network trained on EEG data for the understanding and prediction of functional changes in the brain: A case study on opiate dependence treatment, Neural Networks, vol.68, 62-77. doi:10.1016/j.neunet.2015.03.009
Doborjeh, M. G., Wang, G. Y., Kasabov, N. K., Kydd, R., & Russell, B. (2016). A spiking neural network methodology and system for learning and comparative analysis of EEG data from healthy versus addiction treated versus addiction not treated subjects. IEEE Transactions on Biomedical Engineering, 63(9), 1830-1841.
Taylor, D., Scott, N., Kasabov, N., Capecci, E., Tu, E., Saywell, N.,Chen, Y., Hu, J., Hou, Z. G. (2014, July). Feasibility of neucube snn architecture for detecting motor execution and motor intention for use in bci applications. In Neural Networks (IJCNN), 2014 International Joint Conference on (pp. 3221-3225). IEEE.
Capecci, E., Espinosa-Ramos, J.I., Mammone, N., Kasabov, N., Duun-Henriksen, J., Kjaer, T. W., Campolo, M., La Foresta, F., Morabito, F.C. (2015). Absence Epilepsy Seizure Data in the NeuCube Evolving Spiking Neural Network Architecture. International Joint Conference on Neural Networks (IJCNN). Killarney, Ireland, 12-17 July. IEEE.
Capecci, E., Doborjeh, Z. G., Mammone, N., La Foresta, F., Morabito, F. C., Kasabov, N. (2016, July). Longitudinal study of alzheimer's disease degeneration through EEG data analysis with a NeuCube spiking neural network model. In Neural Networks (IJCNN), 2016 International Joint Conference on (pp. 1360-1366). IEEE.
Kasabov, N., Scott, N. M., Tu, E., Marks, S., Sengupta, N., Capecci, E., Othman, M., Gholoami Doborjeh, M., Murli, N., Hartono, R., Espinosa-Ramos, J. I., Zhou, L., Alvi, F., Wang, G., Taylor, D., Feigin, V., Gulyaev, S., Mahmoud, M., Hou, Z. G., Yang, J. (2016). Evolving spatio-temporal data machines based on the NeuCube neuromorphic framework: design methodology and selected applications. Neural Networks, 78, 1-14.
FIGURE. Example of a step-wise connectivity evolution in the spiking neural networks model. The activity is recorded during spike-timing dependent plasticity learning (from the initial randomly generated connections until unsupervised training is finished). The resting EEG data was collected during resting and with the eyes open. The data belongs to three groups of subjects: healthy subjects, people with opiate addiction and patients under methadone treatment. The figure shows the projection only of the 3D spiking neural networks model Blue lines are positive connections (excitatory synapses) generated, while red lines are the evolved negative connections (inhibitory synapses). In yellow are the input neurons with their labels corresponding to the 26 EEG channels (From Capecci et all., 2015).
For this project, an R&D system has been developed based on NeuCube. The system can be obtained subject to licensing agreement.