Use of Kohonen Maps as Feature Selector for Selective Attention ...

Use of Kohonen Maps as Feature Selector for Selective Attention Brain-Computer Interfaces Miguel Angel Lopez, Hector Pomares, Miguel Damas, Alberto Prieto, and Eva Maria de la Plaza Hernandez Department of Computer Architecture and Computer Technology University of Granada {malopez,hpomares,mdamas}@atc.ugr.es, [email protected]

Abstract. Selective attention to visual-spatial stimuli causes decrements of power in alpha band and increments in beta. For steady-state visual evoked potentials (SSVEP) selective attention affects electroencephalogram (EEG) recordings, modulating the power in the range 8-27 Hz. The same behaviour can be seen for auditory stimuli as well, although for auditory steady-state response (ASSR), it is not fully confirmed yet. The design of selective attention based braincomputer interfaces (BCIs) has two major advantages: First, no much training is needed. Second, if properly designed, a steady-state response corresponding to spectral peaks can be elicited, easy to filter and classify. In this paper we study the behaviour of Kohonen Maps as feature selector for a selective attention to auditory stimuli based BCI system. Keywords: Brain-computer interfaces, Artificial Neural Networks, SelfOrganizing Maps (SOMs), Selective attention, Auditory Steady-state Response.

1

Introduction

Many types of BCIs have been developed based on the classification of different features extracted from EEG recordings. For example, BCIs based on Eventrelated brain potentials (ERPs) are one of the most popular. ERPs are indicators of brain activities that occur in preparation for, or in response to, discrete events [1]. The P300 is an ERP with a typical latency exceeding 300 ms that shows up after the stimulus is presented and a cognitive task, typically counting target stimuli, is performed. One of the reasons for using the P300 in BCI systems is because it is a large ERP with maximum amplitude in the range of units of microvolts, big enough to be detected even in single-trial experiments [2]. Other BCIs are based on the voluntary modulation by the subject of spectral bands, such as alpha (8-13 Hz), beta (14-20) Hz or theta (5-8 Hz). One of the first BCIs used the spectral power of alpha band as a feature to extract and classify, based on the assumption that human beings can easily modify it. Recently, BCIs based on selective attention to visual stimuli that elicit SSVEP have been developed [3]. ´ J. Mira and J.R. Alvarez (Eds.): IWINAC 2007, Part I, LNCS 4527, pp. 407–415, 2007. c Springer-Verlag Berlin Heidelberg 2007 

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The SSVEP is a periodic response elicited by the repetitive presentation of a visual stimulus, at a rate of more than 6 Hz. SSVEP power extends over an extremely narrow bandwidth as the periodicity of the response matches that of the stimulus [4]. The SSVEP amplitude is substantially increased when attention is focused upon the location of the flickering stimulus and it is more pronounced in recordings over the posterior scalp contralateral to the visual field of stimulation in the range 8.6-28 Hz [5]. SSVEP based BCIs measure the spectral power at flicker frequency in order to discriminate whether the stimuli is attended or ignored. BCIs based on selective attention to auditory stimuli that elicit ASSRs have not been reported yet. ASSRs are composed of a train of superimposed auditory brainstem responses, that added in phase, conforms an averaged response with most of the energy located around the frequency of repetition [6] (see Fig. 1). Treatment of ASSRs signals have two major drawbacks: On the one hand the low amplitude, typically in the range of hundreds of nanovolts, and on the other hand it is not clear yet the influence of selective attention on signals as auditory brainstem responses not generated in the cortex. Peripheral effects of selective attention would only occur if the auditory system is ”obliged” to do so adapting for the most efficient result at the lowest energetic cost [7]. That could happen in a very noisy environment with a very weak auditory stimulus.

Fig. 1. Rows one to four are simulated potentials evoked during the first 100 msec after four auditory click stimuli, delayed 25 ms interstimulus, were applied. Last row shows the averaged sum. ASSRs are generated, in a similar way, as an averaged sum of single ABRs. This figure has been adapted from [6].

BCIs based on classification of features extracted from EEG recordings have some problems in common. First, the target features are immerse in low SNR. That is a weakness as the classification and extraction of the target features is difficult and not always successful. This issue can be minimized either by using high energy features with amplitudes in the range of microvolts, or by grand-averaging many trials as the SNR increases with the number of trials according to equation 1, where N is the number of trials averaged.

Use of Kohonen Maps as Feature Selector for Selective Attention

SNRnew(dB) = SNRoriginal(dB) + 10log10(N) .

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(1)

Another issue is the low average transfer rate. Currently, a throughput of 27.15 bits/min has been reported for SSVEP based BCIs [8]. In order to improve the transfer rate a classification based on several features extracted simultaneously from EEG, or the use of contextual information, when available, have been proposed [9]. A third problem is that EEG signals are not considered to be stationary and the design of experiments have to bear in mind that the same experiment on the same subject could produce different results. In order to avoid this problem, adaptive systems such as ANNs, can be used. A BCI based on the simultaneous extraction of several high energy features and its classification by means of an adaptive system seems to be the basis to enhance performance. This paper shows the use of a Kohonen map as a feature selector. The features under analysis are spectral power in alpha and beta bands and frequencies of an ASSRs and the possible use in BCIs systems. The organization of the rest of this paper is as follows: Section 2 describes the stimuli and the experimental design. Section 3 presents and discuss the results obtained and finally, in section 4, some final conclusions are stated.

2 2.1

Methodology Recordings and Stimulation

One male subject, 30 years old, with university studies and normal hearing participated in the experiment. The subject remained comfortably sat down in a quiet testing room, isolated from noise and external disruptions. The subject was encouraged to relax and close his eyes in order to reduce the background noise level when the EEG was being recorded. The system used for recording was the Geodesic EEG System 200, by Electrical Geodesic. Each electrode was amplified by 1000. Data was collected at a sampling rate of 250 Hz, filtered with a low-pass filter of 100 Hz bandwidth and digitized using 16 bits per sample. The dense-array Geodesic Sensor Net with 128 channels was inmersed in a container of electrolyte and impedance was reported below 5 kOhms. Despite the reference, commonly named channel 129, is located in the vertex for symmetry reasons, it was changed during analysis off-line to the left mastoid (sensor 57). Once applied, a test for electrolyte bridge detection was also performed. Fifteen electrodes were used at positions 6, 13, 31, 38, 54, 62, 80, 88, 106, 113, 7, 32, 55, 81, 107 of Geodesic Sensor Net. Ten of those electrodes match the positions FCz, FC1, C1, CP1, Pz, CP2, C2, FC2, CPz and Cz of standard 10-20 whereas five of them do not have equivalent positions (see Fig. 2). The auditory stimuli were presented simultaneously to both ears through insert earphones at comfortable level, between 50 to 60 dB. Each stimulus consisted of a carrier, 1kHz for left ear and 2.5kHz for right, 100 per cent amplitude-modulated (AM) by a pure tone, 38 Hz for left ear and 42Hz for right, applied during 42 to 46 seconds. This kind of stimulus elicits an ASSR with spectral peaks around the

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Fig. 2. On the left: Central top view of electrodes position. In dark circles the electrodes labelled according to the standard 10-20. In grey the specific electrodes for Geodesic Sensor Net. The electrodes used for training and evaluation were FCz, FC1 C1, CP1, 54, Pz, 79, CP2, C2, FC2, 31, CPz, Cz, 7, 106. On the rigth: Geodesic Sensor Net properly positioned and adjusted. A towel was used to prevent the leak of electrolyte to disturb the subject.

Fig. 3. Rectification of an AM modulated stimulus in the internal auditory system. It illustrates that the response to an AM modulated stimulus is a spectral peak at modulating frequency. This figure has been adapted from [11].

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frequencies of the modulating tones [10]. That is the expected behaviour as the auditory system acts like an envelope detector, similar to an AM demodulator (see Fig. 3) In order to facilitate selective attention, the AM modulated stimuli were used to codify different meaningful, but unknown by the subject, Morse messages. The subject received some training in Morse before the test was executed. 2.2

Experimental Design

The experiment was performed in 2 sessions with 10 minutes intersession rest and 10 trials per session. Each trial consists of one question with binary answer (Yes/No). Each question was displayed on the screen, in front of the subject, for 10 seconds at a comfortable distance and height. Afterwards, two auditory stimuli as described before were presented simultaneously to both ears. The subject was instructed to focus attention to stimulus from left ear if the answer was Yes and ignore the stimulus from right ear. If the answer was No, the subject had to ignore both stimuli. Due to the design of the stimuli and the experiment, it is expected to cause two effects during the attended condition: First, an increase of spectral power in alpha band and a decrease in beta band. Second, enhancement of spectral power of AM modulating frequency for the left ear (38 Hz), although the second effect is not truly confirmed yet. Fig. 4 shows data in the frequency domain collected during a trial in electrode Cz. On the left we see the EEG spectrum up to 45 Hz whereas on the right we see the ASSRs with two peaks at both AM modulating frequencies, 38 and 42 Hz for left and right ears respectively. As selective attention is an inherent feature of human beings, the subject did not experiment much difficulty to focus attention to the target stimulus and to ignore the other one. Only a little training was needed for Morse code. Despite the subject was told to decode the Morse message each trial, the real purpose of the message was help the subject to focus attention. Correct decoding of the message was irrelevant to this experiment.

3

Results

The FFT was computed to measure spectral power in alpha and beta bands and for the ASSR at frequencies 38 and 42 Hz. Noise levels in weak ASSRs recordings were reduced increasing the duration of the trials up to 44 seconds. However in order to avoid start-stop problems due to the nature of selective attention, only the central data of each trial was submitted to Fourier analysis. All collected data during both sessions were used to train and evaluate the SOM. Trials with amplitude bigger than 50 microvolts were rejected, to avoid muscular artifacts. We used a Kohonen SOM to classify the features. An array of 16-by-16 neurons was arranged. For training we used data from fifteen electrodes collected along twenty trials in both sessions. That makes 300 input vectors. Each 4-dimensions vector is composed of: The spectral power of alpha and beta bands and the two ASSRs at the frequencies of the AM modulating tones (38 and 42 Hz). Once the

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Fig. 4. On the left: EEG recording in the frequenciy domain where most of the power is located around the alpha band. On the rigth: EEG amplitude spectrum of two ASSRs AM modulated at both 38 and 42 Hz. The amplitude of peak at 38 Hz might be modulated due to selective attention in the attended condition. The compared asymmetrical amplitude of both peaks is not significant as it could correspond to the maximum efficiency of ASSRs around 40Hz but closer to the first peak.

Fig. 5. On the left, neurons showing negative values are activated by an input vector associated to attended stimulus. Positive values correspond to trials where the stimuli were ignored. Neurons showing zero value are not activated by any input vector. On the right the values are represented in grey scale with zero as neutral grey.

net was trained, 150 input vectors randomly picked from both sessions (75 for attended condition and 75 for ignored) were presented to the network for their classification. In this way only one neuron is activated for each input vector. As the number of neurons is greater than the number of vectors, some neurons never are activated, whereas some other could be activated for more than one input vector. The idea behind this is to gather input vectors in clusters related to selective attention and subsequently analyze the values of their components. Fig. 5 shows the array of 16-by-16 neurons. Neurons in black correspond with neurons activated by input vectors related to attended stimuli, whereas neurons in white correspond to neurons activated by input vectors related to ignored stimuli. Neurons in grey correspond to neurons not activated by any input vector.

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In order to facilitate visualization of clusters, a simple algorithm was executed to make grey neurons become white or black making the boundaries clearer (see Fig. 6). The SOM shows two clusters of approximately the same area, in black and white, that correspond to the attended/ignored condition respectively. The size of the areas matches the ratio of attended/ignored trials (50% each). The analysis of the components of the neurons along the diagonal shows a relation between the power in alpha band and selective attention according to [14]. Beta seems to have the same behaviour as Alpha and that is not the expected behaviour. For spectral power at 38 Hz we see selective attention to enhance level of attended stimulus. For 42 Hz no clear relations can be assured.

Fig. 6. On the left, the SOM determines two clusters: In black for stimuli attended and white for stimuli ignored. According to the topology of the SOM, the diagonal represents the direction of maximum variation in the attended/ignored condition. On the right the values of the four components along the 16 neurons of the diagonal.

4

Conclusions

In this paper we have presented a study of the behaviour of a Kohonen map used as feature selection for a selective attention to auditory stimuli based BCI system. Four different features extracted from EEG were submitted to analysis: The spectral power of alpha and beta bands and the two ASSRs at the frequencies of the AM modulating tones (38 and 42 Hz). As it has been reported in previous papers, we have seen evidence of modulation of spectral power in alpha according to [13,14], beta bands and attended ASSR by selective attention. That is an advantage for BCI systems as selective attention hardly needs the subject to be trained. However we have to keep in mind that this experiment has been executed only in one subject, hence no closed conclusions can be stated. We plan to execute the experiment in a more significant number of subjects. In further studies we will add more EEG features such as the ERPs N100 and P300 to the four features studied in this paper as they are expected to be highly influenced by selective attention [15].

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Acknowledgments. The authors would like to thank the Department of Experimental Psychology and Physiology of Behavior of the University of Granada for the support in the design and execution of the experiment.

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