The importance and difficulty of ground truth generation for EEG data analysis through machine learning

As you may know if you have been following this blog ground truth plays a very important role in the implementation of machine learning algorithms for EEG data analysis. Machine learning includes several adaptive procedures. At the core of supervised algorithms is the concept of learning by example. We show different examples of possible EEG […]

7 metrics of consciousness levels based on EEG

A few days ago, we posted in the blog a discussion on consciousness. We gave an overview on the definition of consciousness. While humankind has struggled with this formidable question for millennia, recent developments in neuroscience study, model, and aim to quantify consciousness. It is precisely this last aspect that raised my interest these days, […]

Multimodal Stress Classification based on Data Fusion

In his blog post a few weeks ago Alejandro Riera talked about characterizing stress based on EEG[1]. The post presented the generation of EEG features based on ratios and differences at particular frequency bands. I would like to comment today on the second part of the story, that goes from such features to the classification […]

One of my favourite playgrounds for Machine Learning on EEG

I would like to comment on two of my favourite application fields of Machine Learning, namely Computer Vision and Brain-Computer Interfaces. Some groups are currently working on its combination. The background idea is that you combine the capability for finding out visual patterns in images of both modern Computer Vision algorithms and of the human […]

Leave-pair-out for EEG analysis through Computational Intelligence/Machine Learning

I have reviewed (last post) some techniques for evaluating the performance of computational intelligence / machine learning systems analyzing EEG signals. I would like to close this series with a post discussing the most advanced technique: the leave-pair-out method. After having discussed K-cross-fold-validation (K-CFV), hold-out (HO), and leave one-out (LOO), I have thought readers would […]

Two alternatives for performance evaluation in EEG analysis through Computational Intelligence

In my recent post “How good is my Computational Intelligence algorithm for EEG analysis?” we introduced the basic procedure and goal for evaluating the performance of a computational intelligence / machine learning system devoted to the analysis of EEG signals. Please refer to that post for the introductory part. We discussed there the basic framework […]

Malasyan Computational Intelligence

Last week I had the chance of training a student group on Computational Intelligence and Machine Learning for EEG data analysis at the Petronas Technology University in Malaysia. As usual, being in direct contact with the research offspring is a very interesting experience. It looks like EEG monitoring is booming in Asia and I could […]