**1. Introduction**

298 Fuzzy Inference System – Theory and Applications

[24] Webpage8, Michael D. Heath, (Florida, Mayo de 1996), "A Robustal Visual Method for

http://marathon.csee.usf.edu/edge/edge\_detection.html

[26] Webpage8,. http://www.hafsamx.org/his/index.htm

Assessing the Relative Performance of Edge Detection Algorithms", Available:

The brain-computer interface (BCI) work is to provide humans an alternative channel that allows direct transmission of messages from the brain by analyzing the brain's mental activities [1–7]. The brain activity is recorded by means of multi-electrode electroencephalographic (EEG) signals that are either invasive or noninvasive. Noninvasive recording is convenient and popular in BCI applications so it is commonly used. According to the definition suggested at the first international meeting for BCI technology, the term BCI is reserved for a system that must not depend on the brain's normal output pathways of peripheral nerves and muscles [2]. It has become popular for BCI systems on motor imagery (MI) EEG signals in the last decade [8]. It reveals that there are special characteristics of event-related desynchronization (ERD) and synchronization (ERS) in mu and beta rhythms over the sensorimotor cortex during MI tasks by discriminating EEG signals between left and right MIs [9, 10]. ERD/ERS is the task-related or event-related change in the amplitude of the oscillatory behavior of specific cortical areas within various frequency bands. An amplitude (or power) increase is defined as event-related synchronization while an amplitude (or power) decrease is defined as event-related desynchronization. As other event-related potentials, ERD/ERS patterns are associated with sensory processing and motor behavior [2]. The principal objective of this study is to propose a BCI system, which combines neuro-fuzzy prediction and multiresolution fractal feature vectors (MFFVs) with support vector machine, for MI classification.

A model is used for time series prediction to forecast future events based on known past events [11]. A variety of methods have been presented in time series prediction, such as linear regression, Kalman filtering [12], neural network (NN) [13], and fuzzy inference system (FIS) [14]. Linear regression is simple and common, but it has less adaptation. Kalman filtering is an adaptive method, but intrinsically linear. The NN can approximate any nonlinear functions, but it demands a great deal of training data and is hard to interpret. On contrary, FIS has good capability of interpretation, but its adaptability is relative low. FISs are fuzzy predictions that can learn fuzzy "if-then" rules to predict data. They are readable, extensible, and universally approximate [14]. Adaptive neuro-fuzzy inference system (ANFIS) [15] integrates the advantage of both NN and fuzzy system. That is, ANFIS not only has good learning capability, but can be also interpreted easily. In addition, the training of ANFIS is fast and it can usually converge only depending on a small data set.

Neuro-Fuzzy Prediction for Brain-Computer Interface Applications 301

The EEG data was recorded by the Graz BCI group [19, 28–32]. Two data sets are used to evaluate the performance of all methods in the experiments. The first data sets were recorded from three subjects during a feedback experimental recording procedure. The task was to control a bar by means of imagery left or right hand movements [19, 28, 30, 31]. The order of left and right cues was random. The data was recorded on three subjects – the first subject S1 performs 280 trials, while the last two subjects, S2 and S3, hold 320 trials. The length of each trial was within 8–9s. The first 2s was quiet, an acoustic stimulus indicates the beginning of a trial at *t* = 2s, and a fixation cross + was displayed for 1s. Then at *t* = 3s, an arrow (left or right) was displayed as a cue (the data recorded between 3 and 8s are considered as event related). At the same time, each subject was asked to move a bar by imagining the left or right hand movements according to the direction of the cue. The recordings were made using a g.tec amplifier and Ag/AgCl electrodes. All signals were sampled at 128 Hz and filtered between 0.5 and 30 Hz. An example of a trial for C3 and C4

The second data sets were recorded from three subjects by using a 64-channel Neuroscan EEG amplifier [29, 32]. The left and right mastoids served as a reference and ground, respectively. The EEG data was sampled at 250 Hz and filtered between 1 and 50 Hz. The subjects were asked to perform imagery movements prompted by a visual cue. Each trial started with an empty black screen; at *t* = 2s a short beep tone was presented and a cross '+'

Fig. 1. Flowchart of proposed system.

**3. Experimentation** 

channels is given in Fig. 2(a).

These good properties are suitable for the prediction of non-stationary EEG signals. Therefore, ANFIS is used for time-series prediction in this study.

An effective feature extraction method can enhance the classification accuracy. An important component for most BCIs is to extract significant features from the event-related area during different MI tasks. A great deal of feature extraction methods has been proposed. Among them, the band power and AAR parameters are commonly used [16–19]. Feature extraction based on band power is usually obtained by computing the powers at the alpha and beta bands. The features are then extracted from band powers by calculating their logarithm values [16] or averaging over them [17]. AAR parameters are another popular feature in mental tasks [18, 19]. The all-pole AAR model lends itself well to modeling EEG signals as filtered white noise with certain preferred energy bands. The EEG time series is fitted with an AAR model.

Furthermore, fractal geometry [20] provides a proper mathematical model to describe complex and irregular shapes that exist in nature. Fractal dimension is a statistical quantity that effectively extracts fractal features. In the last decade, feature extraction characterized by fractal dimension has been widely applied in various kinds of biomedical image and signal analyses, such as texture extraction [21], seizure onset detection in epilepsy [22], routine detection of dementia [23], and EEG analyses of sleeping newborns [24]. In this study, discrete wavelet transform (DWT) together with modified fractal dimension is utilized for feature extraction. That is, MFFVs are extracted from wavelet data by modified fractal dimension. MFFVs contain not only multiple scale attributes, but important fractal information.

The support vector machine (SVM) [25] recognizing the patterns into two categories from a set of data is usually used for the analyses of classification and regression. For example, the SVM is used to classify attention deficit hyperactivity disorder (ADHD) and bipolar mood disorder (BMD) patients by proposing an adaptive mutation to improve performance [26]. The SVM is used for seizure detection in an animal model of chronic epilepsy [27]. Since it can balance accuracy and generalization simultaneously [25], it is used for classification in this study.

To evaluate the performance, several popular methods, including AAR-parameter approach and AAR time-series prediction, are implemented for comparison. This chapter is organized as follows: Section 2 presents the materials and methods. Section 3 describes experimental results. The discussion and conclusion are given in Sections 4 and 5, respectively.
