Machine Learning Researcher at Iowa State University

About this employment

Developed an EEG gesture recognition program as part of a senior research project, conducting extensive data analysis and implementing real-time gesture recognition.

Tags: #ideology/machine-learning#programming-language/matlab#programming-language/python
Started: Dec 1, 2021
• 4 min read

Machine Learning Researcher at Iowa State University

Position: Machine Learning Researcher
Company: Iowa State University
Location: Ames, IA
Employment Period: December 2021 - April 2022
Industry: Research / Machine Learning

Overview

As a Machine Learning Researcher at Iowa State University, I conducted independent research focused on developing brain-computer interface (BCI) technology. This senior research project involved creating a system that could recognize hand gestures from EEG (electroencephalogram) signals in real-time, pushing the boundaries of human-computer interaction.

Key Responsibilities

  • Developed an EEG gesture recognition program as part of a senior research project

    • Designed experimental protocols for EEG data collection
    • Built data acquisition pipelines for brain signal processing
    • Implemented signal preprocessing techniques (filtering, artifact removal)
    • Created feature extraction algorithms for EEG signals
    • Developed machine learning models for gesture classification

  • Conducted extensive data analysis and model training

    • Collected and annotated EEG datasets from multiple subjects
    • Performed statistical analysis on brain signal patterns
    • Experimented with various ML algorithms (SVM, Random Forest, Deep Learning)
    • Optimized model hyperparameters for improved accuracy
    • Validated models using cross-validation and real-world testing

  • Implemented real-time gesture recognition

    • Built low-latency signal processing pipelines
    • Optimized algorithms for real-time performance
    • Created visualization tools for monitoring brain activity
    • Developed user interfaces for BCI interaction
    • Implemented feedback mechanisms for user training

Technical Skills Acquired

  • Machine Learning: Classification algorithms, deep learning, model optimization
  • Data Analysis: Signal processing, statistical analysis, feature engineering
  • Real-time Systems: Low-latency processing, streaming data handling
  • Python: Scientific computing with NumPy, SciPy, scikit-learn, TensorFlow
  • EEG Signal Processing: Filtering, artifact removal, feature extraction
  • Research Methods: Experimental design, data collection, scientific writing

Research Outcomes

  • Achieved 85% accuracy in recognizing 5 different hand gestures from EEG signals
  • Reduced gesture recognition latency to under 500ms for real-time applications
  • Presented findings at the Iowa State University Undergraduate Research Symposium
  • Contributed to advancing BCI technology for assistive applications

Impact

This research demonstrated the feasibility of using consumer-grade EEG devices for gesture recognition, opening possibilities for affordable brain-computer interfaces. The system I developed showed potential applications in assistive technology for individuals with motor disabilities, gaming interfaces, and hands-free device control. The project laid groundwork for future research in non-invasive brain-computer interaction at Iowa State University.