An experimental design is programmed using the presentation tool to investigate the global response inhibition process by quantifying the parameters such as inhibition efficiency, stop-signal delay (SSD) and stop-signal reaction time (SSRT) in the stop-signal paradigm. The aim of this study is to explore the response inhibition mechanisms in the left-hand and right-hand responses by using ERP and ERSP results obtained from the EEG data of different subjects. The inhibition efficiency of the right-hand response and left-hand response appears to be independent of each other as there is no significant difference between them. From these results, the inhibition mechanisms corresponding to these two regions of the brain may be viewed as statistically independent processes. Further, we inferred that the response inhibition mechanisms for both left-hand and righthand responses have approximately the same spectral power observation analysis and we conclude that these processes are statistically independent of each other.
This article presents a microcontroller unit (MCU) based simplified discrete wavelet transform (Sim-DWT) algorithm that can detect high-voltage spindles (HVSs) in local field potential (LFP) signals. The Sim-DWT algorithm operates in an 8-bit MCU, 8MHz operating clock and 16 sample points of buffers to detect HVSs with a frequency range of 5−15Hz. The requirement of only sixteen 8-bit sample points as the window length for calculation and no need for a multiplier render the Sim-DWT easy to implement in an MCU with limited hardware resources. The Sim-DWT is applied in an 8-bit MCU with 6mW power consumption (including IO ports) and was tested for detecting LFP signals in vivo. The design methods and the accuracy of three typical types of mother wavelet functions (Haar, DB4, Morlet) in the Sim-DWT were also tested and compared with those of a PC-based system. The experimental results showed that with appropriately designed cMW functions in the Sim-DWT, HVSs could be detected more accurately than they could be in PC-based software. The present study indicates that the optimized HVS detector (Sim-DWT) can be implemented in an 8-bit MCU with limited hardware resources and is suitable to serve as the digital core in a closed-loop deep brain stimulator microsystem in the future.
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