Brain-Computer Interfaces (BCI)


The Brain-Like Artificial Intelligence (BLAI) is pioneered by Prof.Nikola Kasabov and here it is applied to a specific application.

This project develops novel methods and systems for BCI.

Brain Computer Interface (BCI) monitors the neural activities of the brain and translate them to machine commands to control devices such as computers, wheelchairs or robots. Recent studies have shown the feasibility of BCI to assist the activities of daily living in paralyzed patients. NeuCube’s advanced data analytics capabilities on analysing spatio-temporal brain data have shown promising empirical results on decoding motor tasks from Electroencephalography (EEG) signals. This study shows the feasibility of NeuCube Spiking Neural Network architecture for developing a functional Brain Computer Interface platform to enable robot assisted neurorehabilitation through BCI. Through this approach we aim to detect the patient’s intention to move his or her hand and pass the command to control an exoskeleton or Functional Electric Stimulation (FES) system.

Decoding movements of the same limb is an important problem in BCI for Neurorehabilitation. Due to the non‐invasiveness and high temporal resolution, EEG has been widely used for decoding movements in BCI. However, less spatial resolution caused by the limited number of electrodes is a challenge for pattern recognition. Previous studies on neural activities in motor related areas of the brain during physical movements provide evidences that approximately the same area of brain is activated during the movements of the same limb. Thus, classification of movements of same limb from EEG results less accuracy and limits applicability of BCI for Neurorehabilitation. The state-based online classification module of NeuCube addresses this limitation and facilitates a BCI platform for Neurorehabilitation. The module facilitates a brain state based classification of EEG signals using Spiking Neural Networks (SNN). The module encloses a Finite State Machine (FSM) which acts as a finite memory to the model and a biologically plausible NeuCube SNN architecture to decode state transitions over the time. We performed offline training of NeuCube model using pre-recorded EEG data through Spike Time Dependant Plasticity (STDP) learning rule. The trained NeuCube model is then used to perform online classification on EEG data streams using the Dynamic Evolving Spiking Neural Network (deSNN) classifier. The module follows the cue based (synchronous) BCI paradigm. The subject is required to perform a motor task (i.e. hand opening, hand closing) according to the cue displayed on the screen. While the subject is performing the task, EEG signals are recorded and classified. This classification output is used to control the rehabilitation robot arm. This approach enables the user to control the rehabilitation robots through their own thoughts and intentions and provides neurofeedback to help patients to improve their brain functions.

Development of Spiking Neural Network based Computational Models and Algorithms for Brain Computer Interface’s

Two computational models that use Spiking Neural Networks were proposed to address the limitations in current approaches for BCI control. These models are:

  1. FaNeuRobot: We propose a ‘brain-inspired’ framework for continuous motor control through Brain Computer Interface using Finite Automata Theory and NeuCube evolving SNN architecture. This model addresses the less spatial resolution of EEG particularly evident during different movements of the same upper limb.
  2. eSPANet: Evolving Spike Pattern Association Networks for Spike-based Supervised Incremental Learning
    Due to the non-stationarity and high trial-to-trial variability of brain data, online classification is challenging for BCI. This is more significant when BCI is applied to neurological rehabilitation where the person incrementally learns to regain the control of movement. Here we have proposed a computational model that mimics the incremental learning in biological neural networks using a network of spiking neurons. The model contains a group of Spike Pattern Association Neurons, a spiking neuron model which is able to emit spikes at the desired time-point. According to the stimulus, these neurons are assigned to the relevant neuron population referred as a ‘population vector’. The readout neuron will be trained to produce the corresponding response for the incoming input signals.

FIGURE. NeuCube SNN framework for BCI.

Related Papers and Benchmarking

The proposed methods and systems, when compared with traditional statistical and machine learning methods, showed superior results in the following aspects:

  1. Better data analysis and classification accuracy compared to state of the art machine learning methods such as Support Vector Machine, Multi-Layer Perceptron and Linear Discriminant Analysis (Table1).
  2. Better visualisation of the created models through the dynamic visualization module of NeuCube and as well as with a possible use of VR;
  3. Better understanding of the data and the processes that are measured;
  4. Enabling new information and knowledge discovery through meaningful interpretation of the models.
TABLE 1. Comparative analysis.



Accuracy (%) - Session 1
Accuracy (%) - Session 2
Average Accuracy (%)
50% (+-7%)
80% (+-7%)
75% (+-14%)
92.5% (+-3.5%)

See also some of the related papers:

Kasabov, N. K. (2014). NeuCube: A spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain dataNeural Networks52, 62-76.

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 applicationsNeural Networks78, 1-14.

Hu, J., Hou, Z. G., Chen, Y. X., Kasabov, N., & Scott, N. (2014, August). EEG-based classification of upper-limb ADL using SNN for active robotic rehabilitation. In Biomedical Robotics and Biomechatronics (2014 5th IEEE RAS & EMBS International Conference on (pp. 409-414). IEEE.

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.

Kumarasinghe, k., Owen, M., Taylor, D., Kasabov, N., and  Au, C.K. “FaNeuRobot: A ‘Brain-like’ Framework for Robot and Prosthetics Control using the NeuCube Spiking Neural Network Architecture & Finite Automata Theory,” IEEE International Conference on Robotics and Automation 2018. (Submitted).

Kumarasinghe, K., Taylor, D., and Kasabov, N. “eSPANNet: Evolving Spike Pattern Association Neural Network for Spike-based Supervised and Incremental Learning and Its Application on Single-trial Brain Computer Interfaces,” IEEE Transactions on Neural Networks and Learning Systems. (Submitted).

R&D System

For this project, an R&D system has been developed based on NeuCube. The system can be obtained subject to licensing agreement.


The developers of this project are:

Kaushalya Kumarasinghe