Working Memory Impairments Limitations of Normal Children s in Visual Stimuli using Event-Related Potentials

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1 2015 6th International Conference on Intelligent Systems, Modelling and Simulation Working Memory Impairments Limitations of Normal Children s in Visual Stimuli using Event-Related Potentials S. Z. Mohd Tumari, R. Sudirman Faculty of Electrical Engineering Universiti Teknologi Malaysia UTM Johor Bahru, Malaysia rubita@fke.utm.my Abstract This study emphases to investigate the working memory impairments limitations of normal children s in visual stimuli assessments using Event-related Potentials (ERPs) signal. 97 children aged between 7 to 12 years old were subjected to a two-phase computer based assessment while their working memory activity was concurrently recorded using Electroencephalograph (EEG) 9200 machine at the prefrontal cortex. EEG signals were segmented and averaged into the event of stimulus to obtain the ERPs pattern. In neurophysiology research, ERP component at P300 will forms the visual responsiveness to detect the cognitive processes. P300 component is decomposed using Discrete Wavelet Transform (DWT) with Daubechies (db4) at 7th level decomposition. In conclusion, visual stimuli assessment brings up a dissimilar in P300 pattern between two-phases. The mean performance shows that, the younger children tend to working memory impaired rather than older children. Keywords- working memory; ERP; EEG; visual stimuli; P300 I. INTRODUCTION Working memory is the central processing unit of the nervous system, a temporary memory storage which depends on individual ability. Children having low working memory might be aspect learning difficulties in diverse forms. Low working memory can lead to discrimination by normal children. The learning performance of children depends on the individual s memory storage capacity. It is common that children have difficulty in memorizing a solved task for a long period, and such incapability in coined as working memory impairment. This impairment is systematically quantifiable concluded the brain signals, which permits the formulation of actual approaches that can indicate the severity of working memory impairment in children. The term of working memory is defined as a structure of human brain which is able to store information temporarily and can manipulate some specific information such as complex cognitive ability, for example, language comprehension, learning and solving problems. Moreover, there are two important areas about the nature of working memory where the major area acts as a storage system while the other as the important information which are used in intellectual tasks [1]. The aim of this study is to identify and highlight the ERPs response for two classes of respondents, i.e., pictures, to remember within a few seconds. Some studies have stated that visual pictures can represent a powerful modulator of cognitive performance for training the children to remember in a short period of time [2]. The ordinary children chosen for this visual stimuli assessment are hypothesized to have certain degree of working memory impairment. Furthermore, sensory responsiveness of visual is used to investigate the working memory performance in neural computation of the various functional areas. However, this does not mean that they are abnormal since the comprised development of their working memory could have been caused by their environment, culture, or traumatic events that have distracted their attention during classroom learning [3] [4]. The black and white pictures were chosen because of the color is very instrumental in working memory. The prefrontal cortex area on the brain is chosen to record the ability of working memory performance supported the assessment given. Basically, prefrontal cortex uses to record brain activity for the short term memory. The prefrontal cortex is responsible for organizing thoughts and information as compared to the inferior temporal cortex, which reacts more to stimuli. The prefrontal cortex is the area of the brain that governs our emotion, complex thought, and problem solving. Electroencephalograph (EEG) is used to record the electrical brain activity generates by the brain structures [5]. It reproduces the thousands of the brain responses over a converted stimulus or activities. As the EEG procedure is painless and non-invasive, it is being widely used to study the brain organization of cognitive processes such as perception, attention, language, memory and emotion in normal adults and children. The EEG spectrum generated were within five frequency bands, i.e., delta (< 4 Hz), theta (4 to 8 Hz), alpha (8 to 13 Hz), beta (13 to 30 Hz), and gamma (> 30 Hz) [6]. High impedance should be less than 10 k. The electrode placement system is applied for electrode positioning to achieve standard reproduction at channel (F7, F3, Fz, F4 and F8) for working memory development (see Figure 1). Event-related potential (ERPs) is initiated by an external or internal stimulus and suitable for studying the aspects of cognitive processes of both normal and abnormal nature such as psychiatric disorders and neurological [7]. The resulting of ERP signal would further our knowledge of the neurophysiology of recognition memory [8]. One of the ERP components that are ordinarily investigated in behavioral neuroscience research is positive P300 component. P300 might occur due to allocation of /15 $ IEEE DOI /ISMS

2 attention resources to stimulus attended by memory updating. P300 manifest itself as a positive voltage approximately 300 ms after the stimulus [9]. In consequence, P300 components is chosen to be more expressive behavior for visual stimuli since it most stable signal that occurs when an event stimuli is employed. event). Discrete Wavelet Transform (DWT) is used for the pre-processing of the ERP signals to find the alpha rhythm. This study uses alpha oscillation with frequencies between 8 Hz to 14 Hz to analyses of ERP patterns for normal children working memory performance. The resulting ERP patterns will be compared with the mean performance scoring after the stimulus to disclose the working memory impairment among different ages. The result would further our knowledge of the neurophysiology of recognition memory. II. MATERIALS AND METHODS Figure 1. Medial View of Prefrontal Cortex at the channels: F7 and F3 (Left Midline), Fz (Center Midline) and Right Midline (F4 and F8) [14]. Wavelet transform is proposed to perform the time-scale analysis of signal. The advantages of using wavelet transformation are different from Fourier transformation because this type of transformation can capture the transient features in a given signal and provide the corresponding time frequency information. The extracted wavelet coefficients provide a compact representation that shows the energy distribution of the EEG signal in time and frequency domain [10]. One purpose of wavelet transformation is to split the fundamental structure of difference time domain. The Discrete Wavelet Transform (DWT) is preferred to decompose the ERP signal through the means of low-pass and high-pass filtering [11]. The suitable wavelet family selected for this study was Daubechies order of 4 to match the ERP properties which were orthogonal, symmetry, compact support, and non-stationary signals. To acquire sustained attention, ERP signal at alpha frequency is used on the whole study because of alpha rhythm is the most prominent component of brain waves. The frequency and amplitude are known to be affected by external stimuli such as memory task and mental arithmetic. If the underlying mechanism of the alpha rhythm is clarified, it can be applied on the clinical diagnosis for communication with severe physical disabilities by using ERP signal. On the other hand, positive change in ERP alpha increases the speed of dispensation information and improves cognitive performance [12]. The great performance is conducted after the stimulus given; the children need to recall what they remembered [13]. After the visual stimulus presented, the computation of performance scoring among a group of age is examined on the quiet of baseline in each picture. Statistical confirmation of pattern is provided by ((total number of correct total number of subject) 100). This study required to determine if recognition memory can be measured using P300 component in response to screening tools for a generally used visual stimulus. Visual stimuli were specified to the children through two-phases. Phase 1: study phase (four pictures); Phase 2: working memory phase (seven pictures including inter-stimulus A. Subjects 97 children aged 7 to 2 years old are designated in this study. Concerning this determination, 60 subjects was elective have a good result, and 37 subjects have a moderate result in academics. They had no previous history of neurological and mental abnormalities. Interviews were held with the teachers in the pre-study stage to understand the ability and performance of children in the classroom. B. Experimental Setup EEG data were recorded using Neurofax-EEG 9200 and EEG recording can be achieved by placing electrodes on the scalp which attached to the subjects using wires. The sampling frequency is set to 1000 Hz. Scalp electrodes are placed on their head according to the standard Electrode Placement at channels - F3, F4, Fz, F7 and F8 for working memory development. Then, Phase 1 (the study phase) is originated where the subjects were given four black and white pictures. They were asked to study the sequence of pictures and follow the instructions given by the examiner. The task started with the sequential presentation of four different pictures about 5 second per picture and repeated two times. The screen will be automatically back into the fixation block (white color) after all pictures presented. In sequence, their ability to-be remembered the pictures is tested and recorded using an EEG machine. Next stage is the subject need to remember 7 pictures with old and new pictures (Phase 2: the working memory phase). The new picture (inter-stimulus) will be presented for a minimum of 1 second between any two old pictures. The subject need to remember the sequence of pictures and their EEG signal response is recorded. C. EEG Signal Processing EEG signal captured were analyzed using MATLAB software. Firstly, raw EEG signal of working memory performance was filtering the artifact that contaminated on the signals. Since EEG data are recording continuously, if the activity generated in response to visual stimuli, EEG signal activity that not related to the visual stimuli can be categorized as artifacts. The brain signals that corresponded to each stimulus were segmented according to the event responses (Phase 1: 5s, 10s, 15s, and 20s; Phase 2: 5s, 6s, 11s, 12s, 17s, 18s, and 23s). The condition is averaged separately to produce a total of 5000 ERP points. Averaging and segmented is applied to 97

3 find the ERP signal so that a more conclusive diagnosis on the working memory performance of a particular respondent could be made. By doing this averaging at each time point according to the stimulus, we end up with replicable waveform for each stimulus type (see Figure 2). Figure 3. Original signal is decomposed into 7th of level decomposition with detail and approximation coefficient of signal. The original signal x(n) is down-sampling by 2 through a low-pass filter (LP) and high pass filter (HP) and produces output of ca and cd. Figure 2. Example of EEG segmented and averaged into ERPs signal for Phase 1. D. Discrete Wavelet Transform (DWT) Analysis In order to examine the ERP signal that has been recorded, DWT has been implemented to decomposed into several level of decomposition depend on the frequency sampling (F s = 1000 Hz). In accordance with the theory, 7th level wavelet decomposition is selected with mother wavelet of Daubechies of 4 (db4). In this representation, the original signal with frequency of 1000 will be down sampling by 2. The signal passed through a low pass filter (Approximation coefficient: ca) and a high pass filter (Detailed coefficient: cd) and produces output of approximation and detailed coefficient with cut-off frequency. Then, low frequency contents length from (0 to 500) as name as A1 will continue down sampling by 2 which A2 (0 to 250) and high frequency (500 to 1000) as a D1 will down sampling into D2 (250 to 500). Processes of down sampling resolve interchange until the original signals find all the frequency ranges (D5: gamma, D6: beta, D7: alpha, A7: theta). In this case, the alpha frequency is chosen, thus the analysis of down sampled will stop at the level of 7, and alpha frequency indicated at the D7 (8 to 16 Hz) to be processed into ERP signal analysis (see Figure 3 and Figure 4). Figure 4. Example one of the subject (Phase 1): ERP signal decomposition with sub-band D5 to D7 and A7. III. RESULTS AND DISCUSSION ERP signal at alpha frequency is carried out to identify the pattern of children stimuli events according two-phases tasks. The main focus ERP signal is to investigate the children s responsiveness toward visual stimuli concerns their ability to interpret the surrounding environment after processing the information contained in visible light. Analyzing the ERPs can serve cognitive purposes in assessing cognitive responses without recognizable performances, but neurophysiology activity recorded. A comparison of amplitude P300 component between all aged is shown in Figure 7. The obvious dissimilarity in that the 7 and 10 years old children had reached a grand average ERP of about μv and μv more than others aged who had reached lower amplitude at P300 during Phase 2. 98

4 TABLE I. PEAK VALUES IN THE GRAND AVERAGE ERPS FOR DIFFERENT AGES BETWEEN PHASE 1 AND PHASE 2 Age Phase 1 (μv) Phase 2 (μv) The performance on cognitive tasks showed working memory impaired when the task increasing. This also showed that the P300 component had increased with amplitude variability in the visual stimuli responsiveness. Even-though, 7 and 10 years old indicated higher amplitude at Phase 2, but their significantly different only away from zero values. Different subjects give different results, but the grand means for ERPS at P300 are rather close to each other. Phase 2 had slight delay on latency and had lower amplitude rather than Phase 1 when the children were exposed to new pictures. The previous study stated that early childhood has an increasing alpha band, and then starts to decline with age [15]. For children, which aged 1, 3, 10 and 15 years old will increase the alpha band from 5.5 to 8, 9 and 10 Hz. The mean performance percentages are applied to the correct responses through the two-phase stimuli for a group of age (7, 8, 9, 10, 11 and 12 years old). Mean performance was analyzed supported on the total score for each assessment. This score was collected when the children recall the sequence of the pictures. The score of performance divided into two variable (1: if the subject can answer correctly the name of pictures in array; 2: if the subject cannot answer correctly). The analysis of scoring performances is using Microsoft Excel to compute the percentage of each picture in an array. The mathematical formula has been made to analyze the scoring of mean performance per picture (Total number of correct picture/ Total number of subject for a group of age x 100). For example, for 7 years old children, only one subject out of 15 cannot answer correctly the first picture in Phase 2 thus the score performance is (14/15) x 100 = 93.3 %. Regarding on analysis, the mean percentages of correct responses on change trials for each age group and array picture are presented in Figure 5 for Phase 1 and Figure 6 for Phase 2. For the first phases, there is not significantly different for the first three pictures, all subjects in each group can remember precisely, but for the fourth array picture, the mean performances were diverged significantly for the 7 years old. But for children of 2 years old, their working memory performance maintains until the end of the session. The result contrast with the second phases, which the children tend to working memory impaired in the second array pictures and 7 years old shows diverged drastically for the fourth array picture. Within age groups, the performance of 2 years old shows significantly different with the seven array pictures. It can be seen that the mean performance decreased as array size increased. This decrease was most marked in the youngest children and the least for the oldest children. By comparing the result between Phase 1 and Phase 2 stimuli, 7 years old children indicated working memory impaired when the number of stimuli increased. While 12 years old children performed well even-though the occurrence of visual stimuli in assessment increasing. Figure 5. Percentage of mean performance changes is detected by a group of age at each picture in an array for Phase 1. Figure 6. Percentage of mean performance changes is detected by a group of age at each picture in an array for Phase 2. A repeated-measures analysis of variance (ANOVA) also known as two-factor with replication was obtained to approve if there any significant difference between age group and array picture. This performance was recorded with array size of the picture as the within-groups factor and age group as the between-groups factor. In this output of ANOVA shown in Table I, the test statistic, F, is reported in the analysis of variance table with age group, F (5,3) = The p-value for this statistics is p < (reported in the table as ). This means that there is no evidence that there is difference in the means across groups of ages. While, for an array of pictures, F (3, 15) = 6.68; p-value is which smaller than p-value for statistical. Thus, there is significant difference between the array pictures. There was no significant interaction between array picture and age group (in Interaction column), F < F criteria = 1.63, with the p- value is For the Phase 2 shown in Table II, for the age group (Sample row), F > F criteria = 27.16; with the p-value is which smaller than So, we can conclude that there is significant difference between the ages of the group. In the 99

5 Columns row, there was also significant difference among each of array picture with the F is and p-value is While, for the interaction between age group and picture, there have not been significantly altered, since F = which higher than F criteria (1.25) and p-value equal to > TABLE II. STATISTICAL ANALYSIS OF ANOVA (TWO-FACTOR WITH REPLICATION) BETWEEN AGE GROUP (BETWEEN- SUBJECT FACTOR) AND ARRAY PICTURE (WITHIN-SUBJECT FACTOR) FOR PHASE 1 Source of SS df MS F p-value F Variation criteria Sample Columns Interaction Within Total TABLE III. STATISTICAL ANALYSIS OF ANOVA (TWO-FACTOR WITH REPLICATION) BETWEEN AGE GROUP (BETWEEN- SUBJECT FACTOR) AND ARRAY PICTURE (WITHIN-SUBJECT FACTOR) FOR PHASE 2 Source of Variation SS df MS F p-value F crit Sample Columns Interaction Within Total IV. CONCLUSION ERP patterns for Phase 2 (including old and new pictures) that contains 7 pictures elicit greater positive amplitude at P300 component than Phase 1 (4 pictures). This observation proves to have an effectiveness of visual objects embedded in the working memory task based on the Event- Related Potentials (ERPs) signal response is determined. Depending upon the proposed visual stimuli, the advantages of this study are can differentiate working memory performance of P300 ERP signals for each age group. Thus, this study has been overcome the idea to use the P300 ERP signal at alpha frequency to identify the working memory performance. The alpha rhythm pattern in normal children changes due to their ability to memorize picture sequences well. Alpha value for normal children that does not have working memory is greater than normal children who having working memory when both kinds of children were exposed to visual stimuli. Thus, EEG method can be used to record their brain signal in order to monitor their level of visual response through working memory performance. It has been shown that, working memory is only part of cognitive development but there is a capable future method in this area. This study also discussed the developmental trends for age-differentiate, whether normal children with no working memory difficulties have a working memory deficit. By using standardized visual stimuli model for all groups, their scoring of mean performance is recorded after the stimulus responses. 97 children were compared among a group of aged of children with 7 years old children performed worse on Phase 2 with scoring of 33 %. More important, 7 years old children performed worse than the older children (12 years old: 95.3 %), whereas the all group performed worse score on the interstimulus task (representing for 1 second). Compared with the age-differentiate controls, both phases (Phase 1 and Phase 2) performed significantly worse on a second picture in array presented. This finding lends support for the assumption that younger children having working memory impairment. To summarize, working memory performance and behavioral rating correlate significantly by aged for normal children deficit in visual stimuli assessment is discriminated. Overall, this study has provided empirical evidence in support for the assumption that normal children have working memory impaired in young children through visual stimuli assessment. ACKNOWLEDGMENT The author would like to express our gratitude to the teachers for giving us permission to collect the data. The second gratitude is thanked to the MOE, Johor Education Department, Zamalah scholarship and Universiti Teknologi Malaysia for its facilities and funding this study under FRGS (R.J F558). REFERENCES [1] H. Kobayashi, T. Yasuda, and S. Suzuki, Brain Activation during a Manipulative Task and Working Memory Hypothesis, in IEEE International Workshop on Robots and Human Interactive Communication, 2005, pp [2] R. J. Krauzlis, L. P. Lovejoy, and A. Zénon, Superior Colliculus and visual Spatial Attention., Annu. Rev. Neurosci., vol. 36, pp , Jul [3] S. E. Gathercole, T. P. Alloway, C. Willis, and A.-M. Adams, Working Memory in Children with Reading Disabilities., J. Exp. Child Psychol., vol. 93, no. 3, pp , Mar [4] K. Liu and Y. Jiang, Visual Working Memory for Briefly Presented Scenes., J. Vis., vol. 5, no. 7, pp , Jan [5] S. Z. Mohd Tumari, R. Sudirman, and A. H. Ahmad, Study of Normal Children Electroencephalography Signals using Wavelet Transformation, in 2012 IEEE EMBS International Conference on Biomedical Engineering and Sciences, 2012, no. December, pp [6] A. Sanei and J. A. Chambers, EEG Signal Processing. John Wiley & Sons, Ltd, 2007, pp [7] D. Brandeis and D. Lehmann, Event-related potentials of the brain and cognitive processes: Approaches and applications, Neuropsychologia, vol. 24, no. 1, pp , [8] M. Fabiani, G. Gratton, and M. G. Coles, Event Related Brain Potential Method Theory and Applications, in Handbook of Psychophysiology, 2nd ed., J. T. Cacioppo, L. G. Tassinary, and G. G. Berntson, Eds. Cambridge University Press, 2000, pp

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