using independent component analysis to remove artifact from electroencephalographic measured during...

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Using independent component analysis to remove artifact from

electroencephalographic measured during stuttered speech

\

Y. Tran A. Craig P. Boord D. Craig

Department of Health Sciences, University of Technology, Sydney Australia

Abstract--The e/ectro-encepha/ographic (EEG) activity of peop/e who stutter cou/d provide invaluable information about the association of neural processing and stuttering. However, the EEG has never been adequately studied during speech in which stuttering naturally occurs. This is owing, in part, to the masking of the EEG signal by artifact from sources such as the speech musculature and from ocular activity. The aim of this paper was to demonstrate the abi l i ty of independent component analysis (ICA) to remove artifact from the EEG of stuttering children recorded while they are speaking and stuttering. The EEG of 16 male children who stuttered and 16 who did not stutter was recorded during a reading task. The recorded EEG that contained artifact was then subjected to ICA. The results demonstrated that the EEG assessed during stuttered speech had substantial ly more noise than the EEG of speech that did not contain stuttering (p< 0.01). Furthermore, it was shown that ICA could effectively remove this artifact in al l 16 children (p< 0.01). The results from one chi ld highl ight the findings that ICA can be used to remove dominant artifact that has prevented the study of EEG activity during stuttered speech in children.

Keywords--Electro-encephalography, Artifact, Independent component analysis, Stuttering, Speech

Med. Biol. Eng. Comput., 2004, 42, 627-633

J

1 Introduction

STUTTERING IS a developmental disorder that usually appears between the ages of 2 and 8 years (BLOODSTEIN, 1995), with prevalence in children around 1.4% (95% CI: 0.66-2.22) (CRAIG e t al., 2002). The most recent theories on stuttering consider it to be a disorder that involves neural deficits in speech motor activity (BLOODSTEIN, 1995; CRAIG, 2000; HULSTIJN et al., 1997). This deficit is probably inherited (YAIRI e t al., 1996) and results in a stutter, which is an involuntary disruption in fluency during speech.

The moment of stuttering is characterised by this involuntary disfluency that can often be embarrassing (BLOODSTEIN, 1995) and typically involves a struggle to speak. The struggle consists of a speech pattern that can contain times in which the child fails to make any sound (called blocking), repetitions of initial syllables of words and prolongations of words. The speaker's speech musculature also tenses more during non-stuttered

correspondence should be addressed to Dr Yvonne Tran; emaih [email protected] Paper received 22 March 2004 and in final form 10 May 2004 MBEC online number: 20043922

© IFMBE: 2004

Medical & Biological Engineering & Computing 2004, Vol. 42

speech (CRAIG and CLEARY, 1982). Concomitant behaviours can also occur, such as facial distortions, head movements and eye closure.

Because stuttering is now believed to be caused by neural deficits in speech resources (CRAIG, 2000), the electroencephalographic (EEG) activity of people who stutter has the potential to provide invaluable information about the association between neural processing and stuttering. Unfommately, EEG has never been adequately studied during speech where stuttering naturally occurs. This analysis is difficult, because of the large amount of muscular artifact contaminating the EEG from excessive eye activity (blinking or other eye movements), tensing of the face and speech muscles, nodding of the head and so on. Nevertheless, it is crucial for our understanding of stuttering that research is able to investigate brain activity that is associated with speech containing stuttering. Furthermore, as stuttering is a childhood developmental disorder, it is important that this research be conducted in children.

Although there is no research that has investigated the brain activity of children when they stutter, the findings of research conducted on adults who stutter suggests they have significantly different hemispheric speech-motor processing and production from those of non-stuttering adults (BOBERG et al., 1983; BRAUN et al., 1997; FOX et al., 1996; MOORE et al., 1982; SOMMER et al., 2002). This has been shown in

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LEG research (e.g. BOBERG et al., 1983 and MOORE, 1986), as well as in imaging research (e.g. BRAUN et al., 1997; DE NIL et al., 1998; FOX et al., 1996; POOL et al., 1991 and SOMMER et al., 2002). For example, DE NIL et al. (1998) showed greater right cerebral hemisphere involvement in the speech processing of adults who stutter compared with that of adults who do not stutter. SOMMER et al. (2002) showed disruption in com- munication between the left hemisphere speech areas of adults who stutter.

However, both LEG and neuro-imaging studies have been limited in their ability to examine associations between stuttering and neural activity during speech tasks. The above- mentioned problem of muscle artifact associated with the moment of stuttering (INGHAM, 2001) has resulted in research designs that attempted to minimise this prominent artifact. For example, rather than attempt to measure LEG during actual speech that contained stuttering, studies were designed to measure LEG while subjects only listened to spoken language (MOORE, 1986; MOORE and HAYNES, 1980a; b). Researchers using neuro-imaging procedures to study stuttering also face similar difficulties.

Although some imaging studies have attempted to measure neural activity during spoken language (Fox et al., 1996), none has investigated neural activity in children, perhaps because of ethical concerns about exposing children to prolonged radiation and strong magnetic fields. However, as stated above, it is imperative that research be conducted in children, as stuttering is a childhood disorder (BLOODSTEIN, 1995), and neural activity associated with stuttering in adults may well be more influenced by a lifetime of living and coping with the condition than by the developmental aspects of the problem.

in addition, imaging procedures are not yet sufficiently fast to capture the brain activity associated with speech (CONTURE, 2001). For example, the time needed to make a selection from the mental lexicon and produce a verbal response is fast, occurring within 0.04 s (VAN TURENNOUT et al., 1998); however, neuro-imaging procedures can take minutes, or at least more than 1 s in the case of fMRI (KIM e t al., 1997), although faster temporal resolution MRI procedures may be possible in the furore (THOMPSON and MCVEIGH, 2002). Another promising imaging technology is magneto-encephalography, which has been shown to be beneficial for the study of speech (SALUSTRI and KRONBERG, 2004).

Although there have been no studies that have investigated the removal of LEG artifact that occurs during stuttered speech, the artifact that occurs in the LEG recording during normal speech mostly arises from sources close to the brain, such as eye movement, eye blinks and facial or speech muscle activity (BERG and SCHERG, 1994; VIGARIO, 1997; JUNO et al., 2000a; b). in the past, the simplest and most commonly used artifact correction method was rejection, in which portions of the LEG masked by this artifact were discarded (VIGARIO, 1997). However, to use this approach would arguably lead to a significant loss of crucial data that could provide information on the association between stuttering and neural activity. Therefore the authors believe this approach is not suitable for investigating the brain activity of people who stutter.

Another possible strategy for reducing the occurrence of muscle artifact in the LEG involves asking subjects to keep very still while talking and reducing eye movement and blinking by asking them to fix their eyes on a target, requesting they speak in a fashion that minimises jaw and lip muscle tension and so on. However, this strategy is also questionable for stuttering research, especially in the context of studying children who may not be fully co-operative. Furthermore, asking children to control their speech, facial muscles and eye-blinks would probably alter the nature of their stuttering (CRAIG, 2000).

Therefore a new approach is needed if we are to study the association of stuttering and brain activity using LEG.

independent component analysis (ICA) has been used successfully to remove LEG artifact, such as artifacts associated with eye blinks and heart activity. For example, VIGARIO (1997) successfully removed eye blink artifact from the LEG using an ICA algorithm developed by HYVARINEN and OJA (1997). JUNO et al. (2000a; b) successfully removed a variety of artifacts, such as eye blink, line noise and cardiac activity, using the Infomax ICA algorithm (MAKEIG e t al., 1997; 2002) in non-clinical populations and clinical populations with conditions such as autism.

ICA is a signal-processing technique that is currently receiving increasing attention (HYVARINEN and OJA, 1997; HYVARINEN et al., 2001; JUNG et al., 2000b; MAKEIG et al., 1997; NIU et al., 2003; VIGARIO, 1997). it is based upon a reasonable assumption that different physiological sources, such as speech muscles and brain signals, generate maximally unrelated and statistically independent signals (STONE, 2002).

ICA refers to a family of algorithms for source separation based on statistical independence and is an extension o fprincipal component analysis (PCA). ICA generalises PCA to produce independent signals rather than simply uncorrelated signals and was developed to solve the blind source separation problem to recover independent source signals after they have been linearly mixed by an unknown matrix (COMON, 1994; JUNG et al., 2000a; VIGARIO, 1997; POPE and BOGNER, 1996).

ICA discriminates between the underlying source signals, without needing specific information about those signals. It therefore utilises blind source separation (BSS), involving a process of extracting unknown independent source signals from unknown combinations of source signals (JURa et al., 2000a; JUTTEN and HERAULT, 1991; POPE and BOGNER, 1996). it is important to note that ICA is not synonymous with BSS, as BSS techniques are not necessarily based upon statistical independence. The goal of BSS is to invert the mixing function and then recover the sources (ROBERTS and EVERSON, 2001). ICA could therefore be used to separate what are assumed to be independent and unrelated components (such as muscle artifact) from a mixture of LEG data (MAKEIG et al., 1997).

For the ICA algorithm to be effective, there are a few assumptions that need to be met. These are, first, the source signals are assumed to be statistically independent. As artifact signals such as muscular noise are generally not time locked to the synaptic activity of neurons, believed to be the sources of LEG activity, this assumption is satisfied for LEG use. Secondly, the mixing medium is linear, and propagation delays are negligible relative to the inverse bandwidth of the LEG signal, in multichannel LEG, signals from the brain and muscles mix linearly at the scalp. Propagation delays from different sources are also small compared with the wavelength of the signals being studied, so that the signals can be considered to arrive instantaneously. Thirdly, the number of sources is less than or equal to the number of sensors (MAKEIG e t al., 1996; JUNo et al. 2000a; b). This assumption is questionable, as it is not known what the effective number of statistically independent signals is that contributes to the scalp LEG (JUNo et al., 2000a). However, numerical simulations performed by Makeig and colleagues (MAKEIG e t al., 1996) confirmed that the ICA algorithm can accurately identify the time courses of activation and the scalp topographies of large and temporally independent sources, even in the presence of a large number of low level and temporally independent source activities (JUNo et al., 2000a).

Given the success oflCA at removing LEG artifact, including eye blinks, muscle activity and line noise, in a clinical population with conditions such as autism, the authors believe ICA would

628 Medical & Biological Engineering & Computing 2004, Vol. 42

also be capable of removing the prominent EEG artifact occurring in the speech of people who stutter. Therefore the aim of this study was to

(a) characterise the noise that occurs during stuttered speech compared with non-stuttered speech

(b) demonstrate the efficacy of ICA to remove prominent EEG artifact that occurs during stuttered speech.

2 Material and methods

For this study, stuttering was operationally defined as

(i) repetitions that occur in syllables, part or whole words or phrases

(ii) unnatural prolongation of syllables or words (iii) blocking of sounds (iv) unnatural hesitations and interjections (CRAIG et al.,

1996).

Associated concomitant overt symptoms can include eye blinks, facial grimacing, jerking of the head, arm waving and so on. As the child grows older, there is a risk that these concomitant behaviours will become more pronounced. These behaviours are thought to be mostly learned and unconsciously acted and appear to have been adopted by the person who stutters in an attempt to minimise the severity of the stutter (BLOODSTErN, 1995). Usually, the severity of stuttering is best measured using a behavioural assessment (CRAIG et al., 1996) that involves measuring from speech, during a conversation, the frequency of the stuttering (percent syllables stuttered (%SS)) and the speech rate (syllables per minute (SPM)). A %SS of greater than 10%SS and a low SPM (less than 100 SPM; most adults speak at around 200-250 SPM) usually indicate very severe stuttering (frequent stutters and low speech rate).

2.1 Design

The EEG data were gathered in a spoken language condition (reading aloud for 4 min). The participants were asked to keep as still as possible while looking ahead into a blackboard that was positioned about 0.3 m in front of the subjects to reduce any external visual stimuli. For the reading condition, passages of text were standardised for age and pasted onto the blackboard directly in front of the subject. This helped to minimise any head movement artifact. All subjects' EEG activity was assessed while they read the passages out loud for 4 min. So that each participant's face could be seen during the EEG measurements, a video recorder was positioned above the blackboard, and the image was simultaneously displayed on a television monitor. At the same time, a trained observer collected the data and recorded the occurrence of any stuttering.

2.2 Participants

Participants included 16 male children who stuttered, aged between 8 and 14 years (mean age = 11years and 3 months, S D = 2 0 months), as well as a group of 16 male children matched for age who did not stutter (mean age = 10 years 5 months, SD = 16 months). The mean %SS and SPM for the 16 stuttering boys was 12.6 %SS and 143 SPM. The study was approved by the institutional research ethics committee, and participants were only entered into the study after informed consent had been obtained from their parents. ICA was performed on the stuttering and non-stuttering EEG signals in all 32 subjects. Sources of EEG artifact were characterised for all 32 subjects, and comparisons between the two groups were then performed.


To demonstrate further the ability of ICA to remove artifact associated with stuttered speech, one child who stuttered was selected as a representative example. His stuttering was moderate to severe, and so his EEG sample provided a conser- vative test of the ability of ICA to remove EEG artifact associated with stuttered speech. The subject was almost 12 years old and had a moderate to severe stutter, comprising blocking, prolonging and repeating his words, as well as exhibiting secondary features such as eye movements and facial tensing. He stuttered on 13% of syllables spoken in conversation and spoke at 173 SPM.

2.3 EEG procedure

EEG data were recorded using the Neurosearch-24 system*. This system has been used reliably in prior studies (e.g. TRAY et al., (2001)). A total of 20 channels was used, with 19 EEG channels placed in the positions set out by the international 10- 20 Montage system (KLEM, et al., 1999) and one elechomyo- graphy (EMG) channel placed on the surface of the risorius muscle group. The EMG channel was used to confirm speech and stuttering activity and also served as an extra channel that displayed a possible noise component.

All silver/silver chloride electrodes were referenced to linked earlobes, and impedances were kept below 8 k,Q. EEG data signals were acquired at a sampling rate of 128 Hz, and the gain was set at 16,000 to ensure waveform resolution was not lost. EEG data were also analysed using a fast Fourier transform (FFT). FFTs were performed on the data sets so that we could visualise the whole EEG spectrum and observe any reduction in muscular (high-frequency, 14-30 Hz) activity. For the FFT, the EEG signals were subdivided into 2 s epochs that were multi- plied by a four-term Blackman-Harris window and transformed to calculate the magnitude spectrum. Beta band (14-30Hz) magnitudes were calculated as the sum of the components in the FFT for the corresponding frequency range and were averaged over all epochs in a trial. Although 4 min data sets were recorded, only 60 s data sets that contained target artifact were used for the ICA analysis. The 60 s data set was then recompiled to form the original file for FFT and the ICA statistical analysis.

2.4 Characterising sources o f artifact in the EEG that occur during speech

Six sources of EEG artifact arising during the reading aloud condition were characterised by examination of the 60 s EEG samples and included

(a) eye blinks (ocular artifact) (b) muscular tension in the frontal region (e.g. increased

higher frequencies (14 + Hz) (e.g. as a result of frowning) (c) muscular tension in temporal regions (e.g. increased

higher frequency activity as a result of speaking) (d) muscular tension in posterior regions (e.g. increased

higher-frequency activity as a result of behaviour such as blocking on syllables)

(e) muscular artifact in all regions that results in a sudden and brief increase in amplitude and frequency mostly associated with stuttering (e.g. a repetition or prolonging of syllables or from normal disfluencies such as a repeat of a whole word or a re-start of a whole word)

(f) artifact that occurred when the child moved his body and/or head (over all sites).

*Lexicor Medical Technologies, Boulder, CO, USA

629

These sources of artifact were isolated in all subjects, allowing a comparison of artifact between the two groups using the non- parametric Mann-Whitney U-test.

2.5 Independent component analysis

ICA models a recorded signal x as a linear mixture of independent source signals s by an unknown matrix A (JUNG et al., 2000a)

X = {xl( t ) , . . . , XN(t)} = AS

S = {Sl(t) , . . . , SM(t)}

where we assume the number of sources Mis less than or equal to the number of recorded signals N, also described as mixtures.

ICA employs algorithms to determine a square matrix W, specifying spatial filters that invert the mixing process linearly (JUNG et al., 2000a).

u = Wx

This results in ICA components u, identical to the independent sources except for scale and permutation.

Algorithms used for ICA have been well documented, and a number of alternatives exist (HYVARINEN et al., 2001; JUNO et al., 2000b; JUTTEN and HERAULT, 1991; VIGARIO, 1997). The ICA in this study was performed using the Infomax ICA algorithm from the ICA/EEG toolbox (EEGLAB) (MAKEIG et al., 1997; 2002).

There were 19 LEG channels and one EMG channel (that is 20 channels) used in the study. We can estimate 20 independent components from this number of channels, provided we obtain sufficient data (BELL and SEJNOWSKI, 1995; MAKEIG et al., 2002). As the sampling rate used in the study was 128 Hz, at least 10 s o f data were needed for the ICA algorithm, in the current study, 60 s o f each of the LEG files were subjected to the ICA algorithm.

After running the algorithm, the ICA program produced 20 components (as there are 19 LEG and one EMG channels). The data sets o f these components were then plotted as time- series waveforms and topographical maps (indicating the region of the source). Although ICA components have no geome- trical references, the topographical maps are likely to be generated by single dipoles, and thus ICA components are believed to represent focal brain electrical sources (JUNo et al., 2000a). Artifacts from stuttering were identified using temporal, spatial and spectral information. Specifically, the time of occurrence of a stutter was used to isolate a period of data for further analysis.

Artifacts were then discriminated from LEG signals using a combination of spatial and spectral characteristics typical o f artifacts. For example, noise arising from tensing the speech musculature will have sources occurring in the T3-T4 regions, as well as T5-T6 regions. Components displaying these spatial characteristics were subjected to spectral analysis, i f these components contained a large amount of activity in the frequency range above 14 Hz, they were considered likely to be of muscular origin. Knowledge of eye-blink artifacts, namely their characteristic temporal shape, source location near frontal sites and predominance of low-frequency spectral content, was used to identify eye-blink components.

To reconstruct the corrected signal, the corrected LEG signals can be derived as

X' : (W) lU'

where u' is the matrix of activation waveforms u, with rows representing artifactual components set to zero (JUNG et al., 2000a).

Table 1 Mean number o f diffbrent types" o f artifact that occurred in EEG signal while children were engaged in speaking. Significant diffbrences between stuttering and non-stuttering groups" are shown

Artifact source

Non-stuttering Stuttering group group

(n -- 16) (n -- 16)

Ocular artifact (e.g. eye blinks) Muscular tension in frontal

regions (e.g. frowning) Muscular tension in temporal

regions (e.g. speaking) Muscular tension in posterior

regions (e.g. blocking of sounds)

Muscular noise across all regions (e.g. disfluency or stuttering)

Movement artifact across all regions (e.g. shifting positions, moving head)

3.13 4.65 3.00 3.69

5.44 8.19"

1.13 2.75*

3.06 8.50*

1.50 2.62

*p <0.05; *p <0.01

3 Results

Table 1 shows the different types of artifact that occurred in the EEG signal while the children were reading aloud. There were no significant differences between the non-stuttering and stuttering groups for eye-blink artifact, movement artifact and muscular tension in the frontal regions (although trends existed for the stuttering group to have higher amounts of artifact). However, there were significant differences found in muscular tension in the temporal regions ( U = 7 6 , Z adjusted = - 1.98, p < 0.05), in muscular tension in the posterior regions ( U = 68, Z adjusted = -2 .42 , p < 0.01) and in muscular artifact arising from stuttering (e.g. repetitions) and normal speech disfluency ( U = 57, Z adjusted = -2 .69 , p < 0.01).

Table 2 shows the average magnitude (pV) in the higher- frequency region (14 + Hz) for a number of selected sites for the 16 stuttering subjects, before and after ICA. Cortical sites were selected from regions where artifact occurred most, such as the temporal and posterior sites. A paired t-test showed that the magnitude of beta activity was significantly reduced after ICA was used to remove artifact. This was significant in all the temporal sites and the left occipital site. The right occipital site 02 was reduced but not significantly.

For illustrative purposes, Figs 1-5 demonstrate the ability of ICA to remove artifact associated with stuttering. Fig. 1 shows a 5 s portion from the 60 s recording of the EEG time domain collected from the 20 electrodes for the representative stuttering subject during the reading aloud session. This Figure shows

Table 2 Significant diffbrences o f mean magnitude (#V) in frequencies" above 14 Hz before and after ICA removal o f artifact for stuttering group (n -- 16) for selected sites. Standard deviations are shown in brackets"

Mean Mean magnitude magnitude

EEG before after ICA, site ICA, pV pV t-value Probability

T3 28.21 (6.5) 26.02 (6.7) 2.54 <0.05 T4 26.83 (5.8) 24.97 (4.8) 2.10 <0.05 T5 31.23 (7.4) 28.02 (7.0) 4.62 <0.01 T6 33.97 (7.4) 30.10 (7.9) 2.70 <0.05 O1 33.40 (10.7) 30.99 (10.7) 2.54 <0.05 02 32.79 (11.6) 31.76 (11.4) 1.45 not significant

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Fig. 1

0 1 2 3 4

s c a l e 80 ~2

5 t ime , s

5 s EEG sample o f subject before ICA during reading aloud task. Ocular activity from eye blink can be seen in frontal sites" (near 3 s). Muscular tension can be observed in T4, T5 and O1 sites'. Muscular activity from stutter (noted during EEG recording) can be seen at 1 s and between 4 and 5 s (circled). Seconds" are shown on horizontal axis'. Vertical scale is in microvolts

increased amounts of artifact in the signal, associated with muscle movement during stuttered speech, especially between the first (time 0-1) and fifth (time 4-5) s in sites T5, T6 and O1. Eye movement (blinking) artifact is shown in frontal sites at time 2-3 s. There is also artifact associated with increased tension in the jaw in temporal sites.

Fig. 2 shows the 20 components from the ICA projections. The eye-blink component is seen in the first component with the typical ocular artifact. The muscle tension components are shown in component 2, which shows temporal tension, and component 12 shows posterior tension (possibly from blocking of speech). Components 12 and 17 show noise that is typical of a stutter (confirmed by the trained observer).

Fig. 3 shows the corrected EEG. This is the EEG with the above-mentioned components subtracted from the signal. Fig. 4 shows the topographical maps of the components from the ICA projections. This, along with the plot in Fig. 2, assists in the choice of the artifact components. Topographical map 1 shows increased activity in the frontal regions, which is typical of ocular noise. Topographical maps 2 and 17 show increased activity in the T4 and T6 sites, respectively. Map 12 shows increased activity in the O 1 site, which corresponds to the increased muscular tension in the posterior site.

1- 2- 3- 4- 5- 6- 7- 8- 0) 9-

° 1 0 -

o 12- ( ~ 1 3 - - - 1 4 -

1 5 - 1 6 - 1 7 - 1 8 - 1 9 - 2 0 -

d(,,'r,,,~,.,4 'd .,-~ ...... - ~-.-+<,', ,,~.,.a/~'/~ ................... .,.,-'~.., ..................... -~'¢I~ ~ -..~,,.-,~

W

1 2 3 4 5 t ime, S

Fig. 2 20 stable ICA components" generated after performance of ICA analysis on EEG sample o f subject in Fig. 1. This is from 5s sample o f reading aloud task. Component 1 shows" eye blink component with signal that looks" like ocular activity near 3s. Components" 12 and 17 show muscular noise generated from stutte~ Vertical scale in arbitrary units"

P4-4','e' . . . . . w . , s . j " ~ "" ' . , , . /x.>¢,\.

"~,'~'..,.J'l'" ~ "v~a..~',.~"~'~"~<'~"..'.~'."'.~",/"'.,'-/~' '..~..v~.'~...,, "2

i i i i i T+-- 0 1 2 3 4 5

t ime , s

Fig. 3 Corrected EEG after removal o f artifacts. Components" 1, 2, 5, 12, 16 and 17from Fig. 2 were removed. Vertical scale in microvolts

Fig. 5 shows the power spectrum of the O 1 site before and after ICA. There is a high level of power in the frequency region of 14 + Hz before ICA, with the corrected signal showing a reduction is power in this region, associated with removal of muscle artifact originating in the occipital region. The Figure shows that, after ICA, the corrected signal had reduced high- frequency activity above 14 Hz.

4 D iscuss ion

This study demonstrated quantitatively that there is significantly more EEG artifact in male children who stutter during a reading aloud task than in children who do not stutter. These differences arise from muscle artifact associated with stuttered speech. The differences found in the EEG signal were associated with stuttering behaviour, with typical patterns including

(i) blocks, repetitions or prolongations (ii) speech musculature tension

1 2 3 4 5

@ 0 0 0 @ 6 7 8 9 10

11 12 13 14 15

16 17 18 19 20 5

Fig. 4 20 topographical maps of above components'. These maps are used together with components" in Fig. 2, to assist in process of isolating noise components'. Component 1 shows" prominent activity in frontal regions and, with Fig. 2, confirms eye-blink artifact. Component 2 shows" strong activity in 7"4 regions, indicating muscular tension artifacts'. This" can also be seen in components" 12 (01) and 17 (T5). Component 5 shows" activity near very front o f head, where EMG channel was placed. Topographical map 16 and component 16from Fig. 2 show noise generated from frontal regions (Ji, om actions such as frown). Scale in arbitrary units"

Medical & Biological Engineering & Computing 2004, Vol. 42 631

Fig. 5

0 10 20 30 40 frequency, Hz

f 50

Activity power spectrum in O1 site before and after ICA in data from representative subject. ( ) EEG spectrum before ICA. (. ..... .) Change in EEG spectrum following ICA. Reduction in beta activity from 15 to 40Hz was due to removal of muscular tension artifact in posterior regions of stuttering boy during reading aloud task There were few observed changes" in O-lO Hz range. Eye blink artifact was not present at this" posterior site, and therefore there were no reductions between 0 and 4 Hz

(iii) concomitant stuttering behaviours, such as facial distortions and eye closure.

Historically, muscle artifact generated during speaking tasks containing stuttering has proved very difficult to remove, as the artifact masks the EEG signal. Prior attempts to deal with this problem have led to what we regard as unsatisfactory solutions, that is, for example investigating brain activity only during non- speech tasks; removing EEG data masked by the artifact associated with stuttering and speaking; or requesting that the subjects speak in an unnatural way to reduce the artifact.

However, we have been able to remove prominent EEG artifact occurring during stuttered speech in all 16 stuttering subjects, using infomax ICA analysis and knowledge of the temporal, spatial and spectral characteristics of ocular and muscular artifacts. Table 2 illustrates the effects that ICA had in removing the muscular artifact. Before ICA, the magnitude spectrum of the EEG showed high levels of high-frequency EEG. After components characteristic of muscle artifact had been identified and removed, the reconstructed signals showed a significant reduction in high-frequency EEG, as we would expect, owing to the characteristic high-frequency content of muscle activity.

This is not simply low-pass filtering, however. The muscle artifact components identified could also have low-frequency content that contaminates the EEG signal and cannot be removed using low-pass filtering. Using temporal, spatial and spectral characteristics to identify the artifacts, we have used ICA to identify statistically independent sources likely to be of muscular origin. With these components removed, the corrected signal had reduced higher-frequency activity above 14Hz; below 14 Hz, the before and after ICA signal overlaps strongly. This suggests that ICA separated the signals by spatial filtering, preserving the spectra associated with stuttered speech. The results of one of the stuttering subjects are provided to illustrate this capability. ICA was able to remove artifact associated with speech, eye movements and stuttering, so that the remaining EEG signal was sufficiently free of noise that it could be reliably analysed.

i f we are to understand the association of stuttering with neural activity, then research must involve more than the examination of adult neural activity during tasks that are not truly speech tasks, such as listening or even artificial fluency- enhancing tasks. Furthermore, to further our understanding of stuttering, research must also study neural activity that occurs during speech production in children. The results reported in this paper suggest that ICA will open new and useful windows into the study of cortical activity in children who stutter. By separating the data recorded at multiple scalp electrodes into a

632

sum of temporally independent components, noise artifact such as muscular and ocular sources can be successfully removed from the EEG signal.

The results demonstrate the efficacy of ICA in removing speech and stuttering-related artifact. Nevertheless, we believe that it is still important for the integrity of the EEG data that the children remain as still as possible during recording, in some cases, for instance, where the child substantially moved his upper body around in the chair, the resultant EEG signal was totally masked or clipped by artifact, and ICA was less likely to be able to be used to clean the signal effectively. Notwithstanding the above, this study suggests that ICA removed prominent artifact associated with stuttered speech, allowing assessment of the brain activity of children when they stutter, even when they have secondary features such as severe muscle tension in the face and constant eye movement or blinks.

Previous research suggested that the predominant speech processing activity in the region of the brain of a stutterer is no different to that of those who do not stutter, when in a resting condition (BRAUN et al., 1997; INGHAM et al., 1996). it has been shown that lateral asymmetries exist (MOORE, 1986). That is, whatever is causing the stutter, we see predominant fight-hemisphere speech processing rather than predominant left-hemisphere speech processing in adults who stutter. For the first time, using ICA methods to remove artifact, we will be able to examine hemispheric processing during stuttered speech and compare differences with the processing of children who do not stutter, as well as test for differences over time as a function of treatment.

Acknowledgments" All 32 boys described in this paper were participants in a doctoral study on EEG and stuttering, being conducted by Gillian Carmichael. The authors would like to thank her for her assistance in collecting the data and the two anonymous reviewers for their helpful suggestions. Thanks must also be extended to Dr Arnaud Delorme and the people at the Swartz Center for Computational Neuroscience for their invaluable advice and assistance on the EEGLAB toolbox used in this study.

This research has been supported by competitive grants from Brambles industries and the Foundation Markets for Children.

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Authors" biographies

YVONNE TRAN received her BSc (Hons) in biomedical science from the University of Technology, Sydney (UTS), in 1997, and her PhD in psychophysiology, in 2001. She is currently a Post-Doctoral Fellow for the Key University Research Centre for Health Technologies at UTS. Her research interests axe in the areas of health, biomedical signal processing and psychophysiology.

ASHLEY CRAIG is the professor of behavioural sciences in the Department of Health Sciences, UTS. He received his PhD from the University of New South Wales, in 1986. He was granted am honorary doctorate in 2002, for his research into health and stuttering, from South Western University. He has extensive experience in psycholo- gical and psychophysiological research.

PETER BOORD is completing his doctoral studies in the Department of Health Sciences, UTS. He received his BE in electronic engineering at the University of Adelaide, in 1989. His primary research interests are the analysis of brain activity and the development of brain-computer interfaces.

DANIEL CRAIG is a final-yeax student completing his BE in computer systems engineering at UTS. His interests include signal processing, programming and computer systems.

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