Neuroscience · Machine Learning
Josh Threlkeld
Building at the intersection of neuroscience and machine learning. B.S. Neuroscience, USC. Interested in clinical AI, BCI, and how biosignals become decisions.
Research
Anxiety Classification from EEG Signals Using Machine Learning
Investigates the temporal limits of EEG-based anxiety classification using the OpenNeuro ds007609 dataset. Demonstrates a systematic degradation in classification accuracy as target constructs become more temporally distal — eyes-open/closed states outperform state anxiety, which outperforms trait anxiety — offering a principled reframing of EEG classification ceilings as psychophysiological rather than methodological failures.
Music Genre Classification Using Machine Learning on the GTZAN Dataset
Applies machine learning to the GTZAN benchmark for automatic music genre classification. Identifies classical and metal as top performers due to spectral distinctiveness, while rock and reggae underperform owing to genre boundary overlap. Explains the blues anomaly through harmonic structure and random forest nonlinear feature interactions. Surfaces systematic genre-boundary artifacts in standard benchmarks.
Articles
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About
I'm a neuroscience graduate from USC (B.S., Cum Laude, 2025) working at the intersection of brain science and applied machine learning. My research background spans wet lab molecular work at USC's Zilkha Neurogenetic Institute, where I contributed to a PNAS publication on Fragile X syndrome, and computational projects applying ML to biosignals and audio.
I'm currently building toward roles in clinical AI — particularly companies using ML to close the loop between clinical data and care decisions. Longer term, I'm interested in health and life sciences venture.