Browsing by Author "De Silva, AC"

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    Fully 3d-printed dry EEG electrodes
    (MDPI, 2023-01) Tong, A; Perera, P; Sarsenbayeva, Z; McEwan, A; De Silva, AC; Withana, A
    Electroencephalography (EEG) is used to detect brain activity by recording electrical signals across various points on the scalp. Recent technological advancement has allowed brain signals to be monitored continuously through the long-term usage of EEG wearables. However, current EEG electrodes are not able to cater to different anatomical features, lifestyles, and personal preferences, suggesting the need for customisable electrodes. Despite previous efforts to create customisable EEG electrodes through 3D printing, additional processing after printing is often needed to achieve the required electrical properties. Although fabricating EEG electrodes entirely through 3D printing with a conductive material would eliminate the need for further processing, fully 3D-printed EEG electrodes have not been seen in previous studies. In this study, we investigate the feasibility of using a low-cost setup and a conductive filament, Multi3D Electrifi, to 3D print EEG electrodes. Our results show that the contact impedance between the printed electrodes and an artificial phantom scalp is under 550 W, with phase change of smaller than 􀀀30 , for all design configurations for frequencies ranging from 20 Hz to 10 kHz. In addition, the difference in contact impedance between electrodes with different numbers of pins is under 200 W for all test frequencies. Through a preliminary functional test that monitored the alpha signals (7–13 Hz) of a participant in eye-open and eyeclosed states, we show that alpha activity can be identified using the printed electrodes. This work demonstrates that fully 3D-printed electrodes have the capability of acquiring relatively high-quality EEG signals.
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    item: Conference-Full-text
    Implementation of a large piezoresistive sensor array scanning mechanism based on xilinx zynq apsoc
    (IEEE, 2023-12-09) Warnakulasuriya, A; De Silva, AC; Abeysooriya, R; Adikariwattage, V; Hemachandra, K
    The foremost complication of scanning a large sensor array is the increased number of sensors which generate large volumes of data. Hence, a suitable hardware based implementation is necessary to manage such data efficiently. We formulated a scanning mechanism for a large piezoresistive sensor array using a Xilinx Zynq device and custom developed RTL modules. The zynq device acts as the brain of the scanning mechanism issuing control signals and acquiring ADC readings. Therefore, we developed a scanning mechanism using a combination of Xilinx standard IP cores and custom made RTL modules, and deployed it in a zynq device. Performance of the implemented mechanism depends primarily on the developed adc 0 module. It inherits bulk of the functionality of the developed system. Hence, behavioral simulations were conducted on Vivado design suite with respect to data buffering capability, control signal issuance, data alignment and transmission for the adc 0 module. Subsequent overall analysis conducted on the system indicated that the developed system is efficiently functioning.
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    item: Conference-Full-text
    An in-depth study of ssvep signals against stimulus frequency and distance to the stimulus
    (IEEE, 2018-05) Pathiranage, S; Paranawithana, I; Perera, M; De Silva, AC
    In recent times, Steady State Visually Evoked Potentials (SSVEP) based BCI have gained popularity over different types of brain signals as they prove to demonstrate interesting results. It is important to understand the behavior of brain signals with the change of frequency and distance to the stimuli used to evoke them. In this paper, we have looked at the behavior of the SSVEP signals over 4 varying stimuli frequencies and 4 varying distances between the subject and the stimulus. It was found that the strongest SSVEP were elicited in the lower frequency range around 8Hz and a distance up to 100 cm can have a significant effect on the elicited SSVEP signals.
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    item: Conference-Abstract
    An Optimised stimulus for hearing screening of infants using the auditory brainstem response
    Manamperi, WN; De Silva, AC
    This study presents an effective stimulus which enhances the auditory brainstem responses (ABRs) from the underlying EEG. A range of different types of stimuli are implemented and tested using a modified ABR model which is a mathematical approximation of the auditory pathway that generates the ABR. The optimised stimulus is based on a chirp signal which is designed to compensate the travelling delay caused by the anatomical structure of the cochlea, especially at sound intensity levels near the hearing threshold. Results suggest an average increase of 36% in the amplitude of wave V compared to the chirp which used to design the optimised stimulus.
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    Robust algorithm for objective hearing screening of newborns using automated auditory brain-stem response
    (IEEE, 2016-04) Weerathunge, WAHR; Bandara, DMSL; Amaratunga, MGB; De Silva, AC; Jayasekara, AGBP; Bandara, HMND; Amarasinghe, YWR
    At present, 2 to 4 out of every 1000 births are affected with hearing impairments. In enforcing mandatory hearing screening for neonates, robust algorithms are required to make the process fast and efficient. In this paper, we present a novel algorithm to automate objective hearing screening using auditory brainstem response (referred to as ABR). An effective stimulus delivery mechanism, an efficient signal processing algorithm and an automatic peak detection algorithm are consolidated to reduce test time while maintaining accuracy. Simultaneously, compensation for ambient noise levels in clinical environments as opposed to sound proof environments are also considered. The Chirp Stimulus, Empirical Mode Decomposition and High Curvature Detection have been rigorously verified by MATLAB® simulations for data collected by ADInstruments® PowerLab. The algorithms utilized in screening reduce testing time to 8% of the gold standard hearing screening procedure, i.e. Click Stimulus based synchronized averaging. Moreover, the resultant ABR waveforms acquired are de-noised making them comparatively convenient to diagnose. The findings highlighted in the paper provide a superior methodology for robust and accurate newborn hearing screening compared to existing gold standard procedure.At present, 2 to 4 out of every 1000 births are affected with hearing impairments. In enforcing mandatory hearing screening for neonates, robust algorithms are required to make the process fast and efficient. In this paper, we present a novel algorithm to automate objective hearing screening using auditory brainstem response (referred to as ABR). An effective stimulus delivery mechanism, an efficient signal processing algorithm and an automatic peak detection algorithm are consolidated to reduce test time while maintaining accuracy. Simultaneously, compensation for ambient noise levels in clinical environments as opposed to sound proof environments are also considered. The Chirp Stimulus, Empirical Mode Decomposition and High Curvature Detection have been rigorously verified by MATLAB® simulations for data collected by ADInstruments® PowerLab. The algorithms utilized in screening reduce testing time to 8% of the gold standard hearing screening procedure, i.e. Click Stimulus based synchronized averaging. Moreover, the resultant ABR waveforms acquired are de-noised making them comparatively convenient to diagnose. The findings highlighted in the paper provide a superior methodology for robust and accurate newborn hearing screening compared to existing gold standard procedure.