Pattern Geeks: EEG Signal Processing and Wearable Data Insights Intern

Company: PatternGeeks

Position type: Internship

Location: Remote

About PatternGeeks

PatternGeeks is developing a wearable EEG earpiece for people with epilepsy. Our long-term mission is to use wearable brain-signal monitoring and data science to help predict seizures before they happen.

As part of our research and development, we are also exploring what insights can be generated from EEG and wearable sensor data in non-clinical settings, such as stress, fatigue, focus, sleepiness, relaxation, or changes in mental state.

Role overview

We are looking for an EEG signal processing and wearable data insights intern to help write code that turns raw EEG data from our device into useful features, visualizations, and exploratory insights.

This internship project may focus on non-clinical use cases such as stress, fatigue, focus, relaxation, or general brain-signal patterns rather than seizure prediction. This role is ideal for someone interested in signal processing, neuroscience, biomedical engineering, wearable sensors, machine learning, and health technology.

Responsibilities

1. Write code to process raw EEG data collected from the wearable device.
2. Clean and organize EEG data, including handling noise, artifacts, missing data, and inconsistent recordings.
3. Apply signal processing techniques such as filtering, segmentation, frequency analysis, and feature extraction.
4. Explore EEG-derived features that may relate to stress, fatigue, focus, relaxation, sleepiness, or other non-clinical states.
5. Generate visualizations of EEG signals, trends, patterns, and extracted features.
6. Support the development of reusable data pipelines for EEG preprocessing and analysis.
7. Help compare EEG recordings across time, subjects, device settings, activities, or experimental conditions.
8. Document code, methods, assumptions, limitations, and findings clearly so the work can be reused by future team members.
9. Work directly with the founder/team to test different signal processing approaches and summarize results.

Qualifications

1. Interest in signal processing, EEG, neuroscience, biomedical engineering, data science, machine learning, wearable sensors, or medical devices.
2. Experience coding in Python, MATLAB, R, or a similar language.
3. Familiarity with data analysis libraries such as NumPy, pandas, SciPy, MNE, Matplotlib, or similar tools.
4. Basic understanding of signal processing concepts such as sampling rate, filters, noise, artifacts, frequency bands, Fourier transforms, and time-series data.
5. Ability to work with messy real-world data and troubleshoot data quality issues.
6. Strong analytical thinking and attention to detail.
7. Ability to explain technical findings clearly to both technical and non-technical team members.
8. Ability to work independently and document work carefully.

Nice to have

1. Experience working with EEG, ECG, EMG, wearable sensor data, or other biosignals.
2. Familiarity with EEG frequency bands, artifacts, electrodes, channels, reference signals, and preprocessing workflows.
3. Experience with machine learning for time-series or biomedical data.
4. Interest in stress detection, fatigue monitoring, focus tracking, sleepiness detection, or cognitive-state estimation.
5. Experience with Jupyter notebooks, GitHub, cloud storage, or reproducible research workflows.
6. Interest in epilepsy, seizure prediction, digital health, wearable devices, or neurotechnology.
7. Experience creating clear plots, dashboards, or reports from complex scientific data.

What you will gain

1. Hands-on experience working with real EEG data from an early-stage wearable device.
2. Exposure to signal processing, wearable brain-signal analysis, and neurotechnology product development.
3. Portfolio-ready work in biomedical data analysis, EEG preprocessing, feature extraction, and time-series signal analysis.
4. Experience exploring how raw biosignals can be transformed into usable insights.
5. Direct collaboration with the founder/team on a real-world wearable health technology product.