TENZIN TSERING New York City Metropolitan Area Email: tenzinsering1608@gmail.com LinkedIn: https://www.linkedin.com/in/tenzintsering1608 GitHub: https://github.com/ttsering4 Website: https://tenzintsering.vercel.app PROFESSIONAL SUMMARY Independent Researcher - neural decoding & medical imaging Also: HR & People Operations · Product-minded · AI-assisted developer Decoding motor intention from brain signals, and training in medical imaging working at the intersection of neuroscience and radiologic technology. I'm an independent researcher focused on EEG - the electrical signals produced by the brain. My current work uses deep learning to decode motor intention from raw brain activity, and I'm preparing this research for publication. Alongside it, I'm studying radiologic technology at LaGuardia Community College, training in medical imaging. I care about the same core question in both: how do we read a signal from the human body and turn it into something useful for care? I work independently, teach myself the tools I need, and document everything rigorously. WORK HISTORY Independent EEG Researcher | Independent Research | 2024 — Present Self-directed research decoding motor imagery from raw EEG using deep learning. Preparing manuscript for publication. • EEGNet on BCI Competition IV (~82% accuracy) • Python · PyTorch · Braindecode • Signal preprocessing & model evaluation HR & People Operations | TODO: confirm employer | TODO: confirm dates People operations, onboarding, and workflow design with a product-minded, systems-thinking approach. • Employee lifecycle & onboarding • Process design & documentation • Cross-functional coordination Operations & Retail Leadership | TODO: confirm employer | TODO: confirm dates Store operations, team coordination, and customer-facing leadership in fast-paced environments. • Team leadership & scheduling • Inventory & daily operations • Customer experience Spatial Computing & Immersive Tech | Independent / Field exploration | 2024 — Present Hands-on exploration of AR/VR platforms, spatial interfaces, and human-computer interaction. • VR headset prototyping & demos • Spatial computing research visits • HCI & interaction design EDUCATION Radiologic Technology (A.A.S.) | LaGuardia Community College | 2024 — Present | in-progress Clinical training in diagnostic imaging — X-ray, patient care, and reading anatomical signals from the body. Focus: Medical imaging, Patient positioning, Radiation safety, Anatomy & physiology Self-Directed Study — Neuroscience & ML | Independent | 2023 — Present | in-progress Structured self-study in brain-computer interfaces, building toward independent research publication. Focus: EEG signal processing, Deep learning for BCI, Motor imagery paradigms, Research methodology RESEARCH Decoding Motor Imagery from EEG with Deep Learning I trained a compact deep-learning model (EEGNet) to read raw EEG brain signals and predict which movement a person was imagining — left hand, right hand, or feet — using the public BCI Competition IV dataset. The model reached roughly 82% accuracy on this motor-imagery task. Raw 22-channel EEG recordings from the BCI Competition IV dataset (BNCI2014_001) are band-pass filtered and segmented into motor-imagery trials. Each trial is fed into EEGNet — a compact convolutional neural network designed specifically for EEG (Lawhern et al., 2018) — which learns spatial and temporal filters to extract discriminative brain-activity patterns. The network outputs a class prediction for the imagined movement: left hand, right hand, feet, or tongue. ~82% — Decoding accuracy (BCI Competition IV-2a) EEGNet — Compact CNN architecture 4-class — Motor imagery (left hand · right hand · feet · tongue) 9 — Subjects evaluated PUBLICATIONS Decoding Motor Imagery from EEG Signals Using EEGNet (2025) Tenzin Tsering · Independent Researcher A compact EEGNet model trained on the BCI Competition IV motor-imagery dataset achieves approximately 82% decoding accuracy across four imagined-movement classes, demonstrating that deep learning can extract motor intention from raw EEG without hand-crafted features. PDF: /tenzin-eeg-paper.pdf Code: https://github.com/ttsering4 SKILLS Research & ML: EEGNet, Deep Learning, Signal Processing, Motor-Imagery BCI, Model Evaluation Tools: Python, Google Colab, PyTorch/Braindecode, Matplotlib Domain: EEG, Neuroscience, Medical Imaging / Radiologic Technology KEYWORDS EEG researcher, neural decoding, motor imagery BCI, EEGNet, deep learning, brain-computer interface, BCI Competition IV, signal processing, medical imaging, radiologic technology, neuroscience, independent researcher, HR people operations, Python, PyTorch, Braindecode, New York JOB TITLES Independent EEG Researcher | Neural Decoding Researcher | Motor Imagery BCI Researcher | HR & People Operations Professional | Medical Imaging Student | Radiologic Technology Student CONTACT Open to research collaboration and opportunities in neuroscience and medical imaging.