Eric Easthope

Eric Easthope

I compute the average trial-by-trial power of band-limited speech activity across epochs of multi-channel high-density electrocorticography (ECoG) recorded from multiple subjects during a consonant-vowel speaking task. I show that previously seen anti-correlations of average beta frequency activity (12-35 Hz) to high-frequency gamma activity (70-140 Hz) during speech movement are observable between individual ECoG channels in the sensorimotor cortex (SMC). With this I fit a variance-based model using principal component analysis to the band-powers of individual channels of session-averaged ECoG data in the SMC and project SMC channels onto their lower-dimensional principal components. Spatiotemporal relationships between speech-related activity and principal components are identified by correlating the principal components of both frequency bands to individual ECoG channels over time using windowed correlation. Correlations of principal component areas to sensorimotor areas reveal a distinct two-component activation-inhibition-like representation for speech that resembles distinct local sensorimotor areas recently shown to have complex interplay in whole-body motor control, inhibition, and posture. Notably the third principal component shows insignificant correlations across all subjects, suggesting two components of ECoG are sufficient to represent SMC activity during speech movement.

Eric Easthope

I show that a one-dimensional (1D) conditional generative adversarial network (cGAN) with an adversarial training architecture is capable of unpaired signal-to-signal ("sig2sig") translation. Using a simplified CycleGAN model with 1D layers and wider convolutional kernels, mirroring WaveGAN to reframe two-dimensional (2D) image generation as 1D audio generation, I show that recasting the 2D image-to-image translation task to a 1D signal-to-signal translation task with deep convolutional GANs is possible without substantial modification to the conventional U-Net model and adversarial architecture developed as CycleGAN. With this I show for a small tunable dataset that noisy test signals unseen by the 1D CycleGAN model and without paired training transform from the source domain to signals similar to paired test signals in the translated domain, especially in terms of frequency, and I quantify these differences in terms of correlation and error.