Digital Signal Processing 25 (2014) 164–172 Contents lists available at ScienceDirect Digital Signal Processing www.elsevier.com/locate/dsp Adaptive filtering of EEG/ERP through Bounded Range Artificial Bee Colony (BR-ABC) algorithm M.K. Ahirwal a , A. Kumar a,∗ , G.K. Singh b,1 a PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur-482011, MP, India b Department of Electrical Engineering, Indian Institute of Technology, Roorkee, Uttrakhand-247667, India article info abstract Article history: Available online 8 November 2013 Keywords: EEG/ERP Adaptive filter SNR LMS RLS ABC In this paper, the Artificial Bee Colony (ABC) algorithm is applied to construct Adaptive Noise Canceller (ANC) for electroencephalogram (EEG)/Event Related Potential (ERP) filtering with modified range selection, described as Bounded Range ABC (BR-ABC). ERP generated due to hand movement is filtered through Adaptive Noise Canceller (ANC) from the EEG signals. ANCs are also implemented with Least Mean Square (LMS) and Recursive Least Square (RLS) algorithm. Performance of the algorithms is evaluated in terms of Signal-to-Noise Ratio (SNR) in dB, correlation between resultant and template ERP, and mean value difference. Testing of their noise attenuation capability is done on contaminated ERP with white noise at different SNR levels. A comparative study of the performance of conventional gradient based methods like LMS, RLS, and ABC algorithm is also made which reveals that ABC algorithm gives better performance in highly noisy environment. © 2013 Elsevier Inc. All rights reserved. 1. Introduction As per need of optimization in every field, the emerging tech- nologies play an important role to benefit the science and engi- neering application. The rising complexity has forced researchers to discover possible ways of easing solution of the problems. This motivates the researchers to grasp ideas from the natural organism, and implant it in engineering and sciences. Evolutionary Computa- tion (EC) is a form of stochastic optimization search. It includes Swarm Intelligence (SI) based algorithms and evolutionary algo- rithms. EC is also applicable in area of Computational Intelligence (CI). Algorithms such as Genetic Algorithm (GA), Ant Colony Opti- mization (ACO), Particles Swarm Optimization (PSO), and Artificial Bee Colony (ABC), etc., are all inspired from natural organisms and phenomena’s [1,2]. There are many application areas in which bio-inspired/swarm intelligence techniques are proven effective than the conventional gradient based techniques. Adaptive signal processing and filter de- sign also gets benefited through use of swarm intelligence and evolutionary techniques [2,3]. Filtering of ERP from EEG is one of the well-known application area of adaptive filtering, under adap- tive noise cancellation scheme [4–6]. EEG and ERP signals are more specifically discussed below along with the review of previous * Corresponding author. E-mail addresses: ahirwalmitul@gmail.com (M.K. Ahirwal), anilkdee@gmail.com (A. Kumar), gksngfee@gmail.com (G.K. Singh). 1 Presently, Visiting Professor, Department of Electrical Engineering, University of Malaya, Kuala Lumpur, Malaysia. work and proposed combination of ABC algorithm with adaptive filtering. EEG signals, which are multichannel signals, recorded as brain wave in form of electrical signals, reflect the response of stimula- tion or a task, known as Evoked Potential (EPs), or Event-Related Potentials (ERPs). Stimulation for ERP generation has various types like visual, auditory, and motor movement, etc. [6,7]. ERPs are weak signals buried in signals of spontaneous EEG with very low Signal-to-Noise Ratio (SNR) [7]. ERP in EEG are enhanced and ex- tracted by simple linear methods based on synchronized averaging, power spectral analysis and rectified averaging [8]. Presently, ERP analysis has become a major part of the brain research. These ERPs play an important role in design and development of Brain– Computer Interface (BCI) [8,9]. EEG classification also motivates researchers to explore various states reflected in EEG [10]. Ex- treme Leaning Machine (ELM) is the famous techniques proposed to overcome the slow learning of neural networks; basically neural network is trained with gradient based algorithm [11–13]. Neural network trained with ELM was successfully tested on EEG clas- sification for five mental tasks [10]. Effectiveness of ERP analysis depends only on EEG signal of high SNR value. EEG signals are noisy and non-stationary due to its process of generation from group of neurons. EEG signals are contaminated by artifacts due to line noise, muscle movements, sometime with cardiac signals (ECG), eye blinking and eyeball movements also [7,14]. Therefore, during the past decades, several techniques have been developed for artifact removal from EEG signals [14]. The simplest and most widely applied method for analysis of ERPs is averaging of the measurements over an ensemble of trials, 1051-2004/$ – see front matter © 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.dsp.2013.10.019