چکیده مقاله
Electrocorticography ECoG signals, acquired by placing electrode grids directly on the cortical surface, have gained increasing importance in applications such as brain computer interfaces, epilepsy monitoring, and functional mapping of cortical regions However, ECoG recordings are susceptible to various artifacts, including biological sources such as pulsatile artifacts from blood vessels, as well as environmental noise from the recording equipment Traditional solutions typically involve supervised training to identify noise components for removal In this paper, an unsupervised approach suitable for real world ECoG data, which does not require manual artifact marking, is proposed Initially, FIR and CAR filters are applied to each channel separately, followed by data segmentation and normalization before feeding each channel randomly into the CNN LSTM network individually Achieving an average SNR of 21 71 dB, an average cross correlation of 187 08, an average correlation coefficient CC of 1 00, and an average RRMSE for temporal and spectral domains of 0 09 and 0 07, respectively
کلیدواژهها
نویسندگان
Arash Narimani
۱ M Sc in Electronic Engineering
شیوه ارجاع
Narimani, Arash,1403,Unsupervised Artifact Removal and Signal Reconstruction from ECoG (iEEG) Signals using CNN-LSTM,25th National Conference on Electrical, Computer and Mechanical Engineering,Shirvan
ارائهشده در
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