Projects > Honors thesis

For the past two years, I have been applying my engineering background to computational neuroscience research at the Serre Lab, studying how the visual system functions under Prof. Thomas Serre.

Rapid categorization figure
Decoding accuracy versus time for electrodes in the Occipital lobe for rapid categorization experiments. The lower subplot shows the weights assigned by the linear support vector machine (SVM) to each of the 13 electrodes versus time.

During my first year, I worked on rapid object categorization experiments that investigate how information propagates throughout the brain during object recognition. These experiments relied on advanced multivariate machine learning techniques to decode brain states from electrocorticography (ECoG) and magnetoencephalography (MEG) data from human and non-human primate subjects. From these experiments, I outlined the specific temporal and spatial time courses of visual object recognition along the ventral stream.

The electrode placements for the first and second patients respectively

For my senior honors thesis I decided to investigate neural dynamics during natural vision. I used ECoG data collected from epileptic patients at Rhode Island Hospital who underwent surgical treatment for their condition. Besides being one of the first ECoG initiatives in Rhode Island and at Brown, this experiment promised to break new ground by using natural visual stimuli, unlike the majority of previous visual neuroscience experiments.

The video annotation interface. I adapted computer vision techniques to automatically annotate shot boundaries, detect faces, and track objects.

Instead of simplistic, highly constrained stimuli, patients were shown a movie/film of their choosing while we recorded neural and eye tracking data. My experiment aimed to examine visual processing theories such as object recognition and visual scene processing, using realistic stimuli and advanced machine learning analyses. These multivariate machine learning techniques are needed to parse the overwhelming size and complexity of neural data. In addition, computer vision techniques such as face detection and object tracking were used to automatically annotate the videos shown.

The experimental apparatus. The far right screen is used for video presentation while the others are used for eye tracker calibration and by clinicians.
The experimental apparatus. The far right screen is used for video presentation and has the eye tracker mounted beneath it. The other screens are used by experimenters to control the experiment and calibrate the eye tracker.

I also helped design and build a mobile experimental system that remotely tracks gaze without head constraints and delivers visual stimuli with high temporal precision. In addition to eye tracking data, the system can record local field potentials (LFPs) from clinical subdural macroelectrodes as well as single unit activity from microelectrode arrays. We used this system to record from two patients and the system is currently being used by all other Brown research teams for multi-unit and ECoG recordings.

Decoding for fixations in the left or right visual hemispheres. Notice the results are not significantly different than chance level (50%).
Decoding accuracy for fixations in the left or right visual hemispheres versus time. The blue error bar represents the standard deviation in decoding accuracy. Notice the results are not significantly different than chance level (0.5).

After collection, pre-processing, and synchronizing the data, it came time to do neural decoding analysis to uncover some of the brain mechanisms underlying natural everyday vision. Many analyses were considered to investigate topics such as attentional delay, saccade planning, and human face recognition. However, after months of coding all my results yielded decoding accuracies around chance level. My belief is that the experiment results were compromised by various confounding factors and that perhaps a more controlled experimental paradigm may result better insights.

Despite the poor results, the project taught me a lot about methods in neuroscience research. Thanks to the multidisciplinary nature of this project, I have had the opportunity of collaborating in a meaningful way with neurosurgeons with nation-wide recognition (Rees Cosgrove, Wael Asaad) and prominent computational neuroscience researchers (Thomas Serre, Leigh Hochberg).

You can read the unfinished paper here: Neural dynamics of natural vision (Unfinished due to poor results).

Carl Olsson

Engineer, musician, and lifelong learner. Loves building things