Adaptive Linear Discriminant Analysis Algorithm Applied to Motion Signal Classification in EEG Processing

In view of the current development research on electroencephalograph (EEG) brain computer interface (BCI), which shall not be limited to use linear discriminant analysis (LDA) algorithm only. The brain computer interface that uses multi channel and multi type EEG signal is gradually fusing on other ways that can better reflect the brain activity. Based on the EEG computer interface technology, we can use adaptive linear discriminant analysis (aLDA) algorithm and human-computer interaction method, also regulate adaptive updating coefficient (UC) to reflect the brain thinking activity better. In this paper, we use adaptive common spatial pattern (aCSP) algorithm to extract feature and better classification algorithm to process the EEG, which can improve the efficiency, accuracy and stability of signal classification. Introduction Feature recognition rate on EEG is a core part of brain information classification, because pattern recognition of EEG result shall directly influence the control effect of multi-modal interface [1]. In the part of feature extraction and pattern recognition, using the heavy disability's EEG as the control signal has encountered a problem of parameter dimension, which is insufficient and unable to control accurately. So on the basis of heavy disability's EEG feature, finding the way of better feature parameter extraction to control more appropriately, quickly and accurately is indeed needful [2]. Most of the previous studies on the EEG are for normal people, using the time and frequency domain, parameter model and other methods to acquire feature signal extraction [3]. In addition, when in the different situation, we can regulate UC to get better recognition rate. We use linear discriminant analysis algorithm and adaptive linear discriminant analysis algorithm to process the signal, compare the result and regulate UC to get better result. As we all know EEG has the real-time, large information capacity and flexibility advantages, but it is very susceptible to be influenced by the external environment and also the signal filtering denoising links are very complex [4]. Therefore, choosing the appropriate classification algorithm is very important. To deal with this, use the adaptive way to process the EEG and we find that UC also has a great impact on the classification in the experiment. Denoising Processing and Feature Extraction Use the fourth BCI competition data BCICIV_calib_ds1b, BCICIV _calib_ds1c, BCICIV _calib_ds1e and BCICIV _calib_ds1g to preprocess, but the competition only provides the signal that be processed through the digital analog conversion [5]. As for this, we choose the channel T7 to do filter processing. The reason of using channel T7 was introduced in [6]. Due to the EEG contains too many noises, such as the 50/60Hz power interference artifact, the eye blink artifact, the eye International Conference on Information Sciences, Machinery, Materials and Energy (ICISMME 2015) © 2015. The authors Published by Atlantis Press 413 movement artifact etc [6]. It finds that the motor imagery signal frequency mainly contains between 3 ~ 45Hz Fig. 1: EEG signal processing flow in the experiment, so in order to prevent non-working band signal interference frequency, we need to use the spatial filtering to reduce space fuzzy influence and aCSP algorithm to extract feature, which is widely used in the BCI system based on the motor imagery. For processing two classification of motor imagery problem and this algorithm has a good effect [7]. From a view of mathematical point, the process of aCSP algorithm is the process of solving an optimization problem [8]. Signal Classification Algorithm Time-varying EEG Signal Characteristics. Brain activity status will be influenced by external environment, such as sound, light and physical condition. The impact of factor such as emotion, attention and constant change make the EEG activity have obvious time-varying characteristic [9]. We can see from the Fig.2 to see the time-varying characteristics. Linear Discriminant Analysis. LDA algorithm using training sample for learning to get corresponding linear classification [8]. For example, suppose there is two sample set of 1 Y , 2 Y . The number of 1 Y and 2 Y is n . For each sample data has a * C N Real matrix, C is said the signal channel, N is said the sampling point. The classification training algorithm step [11] is in the following: ( ) 1 1 2 = A m m ω − − (1) ( ) 1 2 1 b = 2 T m m ω ∗ ∗ + (2) ( ) D T y b y ω = + ∗ (3) In this, 1 2 A A A = + , i A is the i class sample covariance matrix. i m represents the i mean sample. i = 1, 2. w, b are the classification parameters. For a sample to be classed the feature vector is y . when 2 D(y) > 0, y Y ∈ . when 1 D(y) < 0, y Y ∈ . Adaptive Linear Discriminant Analysis. Adaptive LDA algorithm uses the new input data to estimate the classification parameter to adapt to the current characteristic of data and improve the rate of correcting classification [10]. Classification of device parameter depends on the covariance A and meaning vector 1 2 , m m , so this algorithm is essentially based on the meaning vector and the covariance. The Estimation of Meaning Vector. For the known N samples in the set ( ) { } y i , the mean m is according to the following formulation definition: ( ) 1 1 N