London, England, United Kingdom
4K followers 500+ connections

Join to view profile

Articles by Matt

  • Top 5 digital trends changing fintech
    Sep 1, 2017

    Top 5 digital trends changing fintech

    Once upon a time, fintech was a trend all by itself. It started with peer-to-peer payment companies like PayPal, and…

    2 Comments

Activity

Join now to see all activity

Experience & Education

  • Amazon Web Services (AWS)

View Matt’s full experience

See their title, tenure and more.

or

By clicking Continue to join or sign in, you agree to LinkedIn’s User Agreement, Privacy Policy, and Cookie Policy.

Licenses & Certifications

Publications

  • Cluster separation index suggests usefulness of non-motor EEG channels in detecting wrist movement direction intention

    IEEE Conference on Cybernetics and Intelligent Systems

    The aim of the study was to select the best electroencephalogram features and channel locations for detection of wrist movement intentions. The detected intentions can be used in brain-computer interfaces (BCIs) either for direct control of an artificial or virtual hand, or they can be used as an underlying binary code for execution of other tasks, 28 channel EEG was recorded while a subject performed wrist movements in four directions. Four basic feature types were extracted in the time and…

    The aim of the study was to select the best electroencephalogram features and channel locations for detection of wrist movement intentions. The detected intentions can be used in brain-computer interfaces (BCIs) either for direct control of an artificial or virtual hand, or they can be used as an underlying binary code for execution of other tasks, 28 channel EEG was recorded while a subject performed wrist movements in four directions. Four basic feature types were extracted in the time and frequency domains for each channel following optimized filtering of the signals. The signals were split into planning and execution segments, respectively. Various delays and anticipation lengths were taken into account for each of the features, thus totaling 93 different features. The potential performance of each feature and channel for use in the classification of the EEG signals was analyzed by estimating the relative class overlap using the Davies-Bouldin index (DBI), a widely used measure for estimating cluster separation. The best feature/channel configurations contained both channels that were close and channels that were far from motor areas. A statistical test using the channel/feature configurations that yielded the lowest 5% DBI values for motor and for non-motor channels yielded no significant difference (alpha = 0.05) between these two channel populations. The scope and depth of the study was limited. Plus, important parts of the signal had to be discarded to rule out interference stemming from saccadic eye movement. However, our results do suggest more attention should be paid to non-motor areas in ear linked EEG data even when investigating movement related BCIs

    See publication

  • 1st order class separability using EEG-based features for classification of wrist movements with direction selectivity

    Engineering in Medicine and Biology Society

    28 channel EEG data were recorded while a subject performed wrist movements in four directions. Four feature types were extracted for each channel following optimized filtering of the signals. The potential performance of each feature and channel for use in the classification of the EEG signals was analyzed by estimating the relative class overlap using a first order histogram approach. The best feature/channel configurations contained channels both that were close and far from motor areas…

    28 channel EEG data were recorded while a subject performed wrist movements in four directions. Four feature types were extracted for each channel following optimized filtering of the signals. The potential performance of each feature and channel for use in the classification of the EEG signals was analyzed by estimating the relative class overlap using a first order histogram approach. The best feature/channel configurations contained channels both that were close and far from motor areas. While the scope and depth of the study was very limited, the results do suggest more attention should be paid to non-motor areas when investigating movement related EEG.

    See publication

Recommendations received

4 people have recommended Matt

Join now to view

More activity by Matt

View Matt’s full profile

  • See who you know in common
  • Get introduced
  • Contact Matt directly
Join to view full profile

Other similar profiles

Explore more posts

Explore collaborative articles

We’re unlocking community knowledge in a new way. Experts add insights directly into each article, started with the help of AI.

Explore More

Add new skills with these courses

See all courses