The use of graphic rules in grade one to help identify children at risk of handwriting difficulties

Previous researches on elementary grade handwriting revealed that pupils employ certain strategy when writing or drawing. The relationship between this strategy and the use of graphic rules has been documented but very little research has been devoted to the connection between the use of graphic rules and handwriting proficiency. Thus, this study was conducted to investigate the relative contribution of the use of graphic rules to the writing ability. A sample of 105 first graders who were average printers and 65 first graders who might experience handwriting difficulty, as judged by their teachers, of a normal primary school were individually tested on their use of graphic rules. It has been found that pupils who are below average printers use more non-analytic strategy than average printers to reproduce the figures. The results also reveal that below average printers do not acquire the graphic principles that foster an analytic approach to production skills. Although the findings are not sufficient to allow definitive conclusions about handwriting ability, it can be considered as one of the screening measures in identifying pupils who are at risk of handwriting difficulties.     

Wavelet Based Filters for Artifact Elimination in Electroencephalography Signal: A Review

Electroencephalography (EEG) is a diagnostic test that records and measures the electrical activity of the human brain. Research investigating human behaviors and conditions using EEG has increased from year to year. Therefore, an efficient approach is vital to process the EEG dataset to improve the output signal quality. The wavelet is one of the well-known approaches for processing the EEG signal in time–frequency domain analysis. The wavelet is better than the traditional Fourier Transform because it has good time–frequency localized properties and multi-resolution analysis where the transient information of an EEG signal can be extracted efficiently. Thus, this review article aims to comprehensively describe the application of the wavelet method in denoising the EEG signal based on recent research. This review begins with a brief overview of the basic theory and characteristics of EEG and the wavelet transform method. Then, several wavelet-based methods commonly applied in EEG dataset denoising are described and a considerable number of the latest published EEG research works with wavelet applications are reviewed. Besides, the challenges that exist in current EEG-based wavelet method research are discussed. Finally, alternative solutions to mitigate the issues are recommended.