Investigation of a wavelet-based neural network learning algorithm applied to P300 based brain-computer interface. Enas Abdulhay * , Rami Oweis, Areej Mohammad, Lujain Ahmad Biomedical Engineering Department, Faculty of Engineering, Jordan University of Science and Technology, Irbid, Jordan Abstract The paper presented herein proposes an algorithm that aims at improving the classification accuracy of Brain-Computer Interface (BCI) speller. In this work, feed-forward neural network with back propagation learning is used for classification purposes. Testing of the proposed algorithm was performed through the utilization of two datasets, namely; Berlin BCI Competition III and EPFL BCI groups. Results, for the first dataset, indicated that the use of 64 electrodes with 30 hidden layers grants an accuracy of 94.9 %, while an average accuracy of 95.8% (range: 92%-100%) was obtained for the second dataset when using a 32 electrode configuration with 20 hidden layers. The obtained accuracy levels, in this study, are higher when compared with other recent classification approaches. Keywords: P300, EEG, Classification, Neural network, Wavelet, Layer. Accepted on May 31, 2017 Introduction People with motor disabilities and neuromuscular disorders such as spinal cord injuries, Amyotrophic Lateral Sclerosis (ALS), and those with "locked in" syndrome are limited in their ability of interaction with the surrounding world. Their motor neurons degenerate; they can no longer send impulses to the muscle fibers that normally result in muscle movement. This in turn results in muscles atrophy; limbs begin to look "thinner" and those most severely affected may lose all the voluntary movements [1]. Therefore, recent advancement in Brain Computer Interface (BCI) encouraged researchers to develop new non-muscular communication channels that allow people with motor disabilities to interact with the environment by effectively controlling communication facilities such as computers and speech synthesizers which would consequently improve their quality of life [2]. Target signals issued from the patient should be translated into commands [3]. The signals can be acquired by collecting brain signals (EEG) [4]. The obtained brain waveforms contain the information needed from the user intentions. EEG measures the brain electrical activity caused by the flow of electric currents during synaptic excitations of the dendrites in the neurons. The EEG signal is measured as the potential difference over time between active and reference electrodes. An extra third electrode, known as the ground electrode, is used to measure the differential voltage between the active and the reference points. The electrodes placed over the scalp are commonly based on the International 10-20 system; it is the most common method for brain signals detection because of its high temporal resolution, relative low cost, high portability and few risks to the users [1]. Since brain activity voltage measured by a given electrode is a relative measure, the measurement may be compared to another reference brain voltage situated on another site. This result in a combination of voltages: brain activity and noise. Because of this, the reference site should be chosen in a site where brain activity is almost zero. In general, there are three referencing methods, Common reference method, Average reference and Current source density (CSD) [5]. There are currently several major categories of BCIs in use that are classified based on the type of neurophysiologic signal they utilize. These categories include, but are not limited to, Visual Evoked Potentials (VEPs), P300 elicitation, alpha and beta rhythm activity, slow cortical potentials (SCPs), and microelectrode cortical neuronal recordings [3]. P300 waves are evoked potentials that are elicited in response to specific stimuli, while SCPs occupy the lowest frequency range of the EEG signal and are associated with cortical activation and deactivation [3]. The BCI system aims specifically at detecting the P300 signal and interpreting it in order to get the user intent. The P300 is a positive deflection in the human EEG, appearing approximately 300 ms after the presentation of rare or surprising, task-relevant stimuli. In P300 based BCI, a matrix of possible successively flashing choices is presented on a screen; and scalp EEG is recorded over the centroparietal area. Main advantage of P300 is the high number of choices. However, only the choice desired by the user evokes a corresponding large P300 potential (i.e. a high amplitude ISSN 0970-938X www.biomedres.info Biomed Res 2018 Special Issue S320 Special Section:Artificial Intelligent Techniques for Bio Medical Signal Processing Biomedical Research 2018; Special Issue: S320-S324