Abstract— Developing efficient and usable brain-computer interfaces (BCIs) requires well-designed trade-off between accuracy and computational time. This paper presents a very fast and accurate method to classify asynchronous brain signals from a multi-class mental tasks dataset using time-domain features. Five different statistical time-domain features were extracted to characterize various properties of three mental tasks electroencephalograms (EEGs). Versatile Elliptic Basis Function Neural Network (VEBFNN) was employed to classify single EEG features as well as multi-feature set. Discriminating power of single features was evaluated and compared by considering the classification accuracy and computational cost consumed during the training stage. Finally, the performance of the best single EEG feature was compared to the multi- feature set. The results indicated the usefulness of Willison Amplitude EEG feature in classifying the different motor tasks as it provided the highest discrimination ratio. Classification results showed the high potential of VEBFNN by the average 89.78% accuracy and 0.21 seconds computation time obtained for its offline training. Moreover, VEBFNN outperformed the conventional support vector machine classifier in both terms of accuracy and speed. I. INTRODUCTION In the past two decades, there has been growing interest for the biomedical and neural engineers as well as the neuroscientists to connect brain signals with computers/machines in order to provide a reliable and comfortable communication pathway for disabled people with external environment. Brain computer interface (BCI) systems aim to detect users’ thoughts and transform them into input commands that control devices like wheelchair and prosthesis hand [1-2]. Various BCI systems using EEG signals have been proposed among which imagination of Motor Imagery (MI)-based, imagination of different limb movements, is one of the most promising [3]. M. H. is with Center for Biomedical Engineering, Transportation Research Alliance, Universiti Teknologi Malaysia, 81310, Johor Bahru, Malaysia (phone: +6014-7730-290; e-mail: hamedi.mahyar@ieee.org). Sh-H. S. is with Center for Biomedical Engineering, Transportation Research Alliance, Universiti Teknologi Malaysia, 81310, Johor Bahru, Malaysia (e-mail: hussain@fke.utm.my). I. M-R. is with David Geffen School of Medicine, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles & Center for Mind and Brain, University of California, Davis, USA (e-mail: irezazadeh@ucdavis.edu). M. A. is with department of Biomedical Engineering, Science and Research Branch, Islamic Azad University Tehran, Iran (e-mail: astarakee@yahoo.ca ). A. M. N. is with Center for Biomedical Engineering, Transportation Research Alliance, Universiti Teknologi Malaysia, 81310, Johor Bahru, Malaysia (e-mail: alias@mail.fkm.utm.my). Imagination of different motor tasks like right/left hand movements generate different EEG features in the brain hemispheres corresponding to specific sensorimotor areas. Such electrical brain activities produced by underlying groups of neurons can be detected non-invasively by surface electrodes. To classify commands from these electrophysiological time-varying signals several stages of processing are necessary. Feature extraction and classification play the most dominant roles for designing BCIs as they directly influence the performance indices of the designed system. Since BCIs have been mainly proposed to be used for online applications, some considerations need to be taken into account like a reliable trade-off between classification accuracy and computational complexity. Although many robust techniques have been introduced for feature extraction and classification of MI signals, they have not considered the trade-off criteria and could not efficiently tackle the observed problems in real-time BCI applications. In terms of feature extraction, more studies have focused on the extraction of spectral, time-frequency, and spatial features like Quantification of Event-Related Synchronization/ Desynchronization (ERS/ERD) phenomenon [4]; Morlet Wavelet transform [5]; Empirical Mode Decomposition (EMD) [6]; Hilbert-Huang Transform (HHT) [7]; Short-Time Fourier Transform (STFT) [8]; Principal Component Analysis [9]; Independent Component Analysis (ICA) [7]; Common Spatial Pattern (CSP) [8]. These features are computationally expensive as they need different levels of transformation which lead to longer processing time. On the other hand, classification problem has been addressed by numbers of linear and non-linear algorithms with different complexity and efficiency such as SVM [9], [10], K-Nearest Neighbor (KNN) [11], and Linear Discriminant Analysis (LDA) [12]. Even though high accuracy might have been achieved by the mentioned methods, computational cost has neither been investigated nor reported vividly. Recently, the effectiveness of time- domain (TD) features for characterizing the MI movements for BCI systems has been investigated [9], [13-14] and it is reported that not only these features are very simple and easy to compute but also they could perfectly represent the discriminative information of different MI EEGs and lead to high degree of classification accuracy. Although very few studies extracted this type of features for MI-based BCIs, it has been shown that they deliver different classification performance when classified by various techniques. In this paper, by considering the TD feature extraction approach we offered a new methodology to provide a reliable trade-off between computational cost and accuracy when EEG signals were recorded asynchronously. Five different Asynchronous Multiclass Mental Tasks Classification through Very Fast Versatile Elliptic Basis Function Neural Network Mahyar Hamedi, EMBS Member, Sh-Hussain Salleh, IEEE Member, Iman Mohammad-Rezazadeh, Mehdi Astaraki, Alias Mohd Noor 2014 IEEE Conference on Biomedical Engineering and Sciences, 8 - 10 December 2014, Miri, Sarawak, Malaysia 978-1-4799-4084-4/14/$31.00 ©2014 IEEE 295