Comparing Common Average Referencing to Laplacian Referencing in Detecting Imagination and Intention of Movement for Brain Computer Interface

Bernard A Conway,Heba Lakany,Syahrull Hi-Fi Syam,M Jusoh,T Sabapathy,Z.I Abdul Khalib,P.J Soh,R.B Ahmad

doi:10.1051/matecconf/201714001028

Bernard A Conway, Heba Lakany + Show 6 more

Open Access

https://doi.org/10.1051/matecconf/201714001028

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Abstract

Brain-computer interface (BCI) is a paradigm that offers an alternative communication channel between neural activity generated in the brain and the user’s external environment. This paper investigates detection of intention of movement from surface EEG during actual and imagination of movement which is essential for developing non-invasive BCI system for neuro-impaired patients. EEG signal was recorded from 11 subjects while imagining and performing right wrist movement in multiple directions using 28 electrodes based on international 10-20 standard electrode placement locations. The recorded EEG signal later was filtered and pre-processed by spatial filter namely; Common average reference (CAR) and Laplacian (LAP) filter. Features were extracted from the filtered signal using ERSP and power spectrum and classified by k-nearest neighbour (k-NN) and quadratic discriminant analysis (QDA) classifiers. The classification results show that LAP filter has outperformed CAR with respect to classification. Classification accuracy ranged from 63.33% to 100% for detection of imagination of movement and 60% to 96.67% for detection of intention of actual movement. In both of detection of imagination and intention of movement k-NN classifier gave better result compared to QDA classifier.

Highlights

Neurological disorders such as amyotrophic lateral sclerosis (ALS), brainstem stroke, brain or spinal cord injury, cerebral palsy, muscular dystrophies and multiple sclerosis impair the neural pathway that control the muscle and /or the muscle themselves [1]
In this paper we present the comparison of detecting motor imagery and intention of movement using 2 different spatial filters namely Common average reference (CAR) and Laplacian filter (LAP)
3.2 Results of Power Spectrum The results for detection of motor imagery are presented in Table 1 and Table 2, while results for detection intention of movement are tabulated in Table 3 and Table 4

Summary

Introduction

Neurological disorders such as amyotrophic lateral sclerosis (ALS), brainstem stroke, brain or spinal cord injury, cerebral palsy, muscular dystrophies and multiple sclerosis impair the neural pathway that control the muscle and /or the muscle themselves [1]. Patients loose voluntary control over their body due to these diseases and are driven to live an isolated, discriminated and restricted life in the long run. Their motor and sensory disabilities unable them to live a normal independent daily life. Despite of losing their voluntary control over their body due to such neurological disorders, which is often a permanent effect, neuro-impaired patients can still communicate with outside world through brain-computer interface (BCI). Number of BCI studies has been conducted in order to build successful interface that applies real-time EEG signal as command to control and /or communicate with the outside world [1]. The most often used spatial filtering methods in BCI studies to enhance the signal-to-noise ratio of EEG signal are Common average reference (CAR) [3, 4, 5] and Laplacian filter (LAP) [6, 7, 8]

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