IMPLEMENTASI ALGORITME LINEAR DISCRIMINANT ANALYSIS UNTUK KLASIFIKASI SINYAL EEG DALAM FPGA BERBASIS PROSESOR LUNAK MICROBLAZE; IMPLEMENTATION OF LINEAR DISCRIMINAN ANALYSIS ALGORITHM FOR EEG SIGNAL CLASSIFICATION IN FPGA BASED ON MICROBLAZE SOFT PROCESSOR

Atmaji, Catur, Agfianto Eko Putra

2015 | Disertasi | FMIPA

It has been developed a MicroBlaze soft-processor on Spartan-6 as a programming base in FPGA for EEG signal classification for two conditions using Linear Discriminant Analysis. Two features used are Slow Cortical Potential and Power Spectrum Estimation using Welch‘s Method. The designed MicroBlaze uses flip-flop and LUTs respectively by 2,453 and 2,675 units and overall occupies 51% of 9,112 slices avalable. All of 32 block BRAM was occupied and resulting 64kbyte of RAM. With frequency sampling of 83.33MHz, computation of SCP and PSE average need 24,787 cycle (297.4 ?s) and 406,317,627.7 cycles (4.88 s). The accuracy of computation in MicroBlaze is 99.35% for mean of SCP and 99.96% for mean of PSE. The testing of EEG signal classification resulting 71.18% of accuracy with SCP features in channel 1 and 2. The addition of PSE feature in channel 6 increasing the accuracy to 77.89% while the addition again of PSE feature in channel 2 increasing the accuracy to 78.26%. This recearch give a sample of implementation of EEG signal classification in lone device such as FPGA. In the future, this system can be used as a model of developing brain-computer interface with several improvements.

Kata Kunci : MicroBlaze; Linear Discriminant Analysis; Slow Cortical Potential; Power Spectrum Estimation