This page contains a brief description of each of my research projects, listed in reverse chronological order.

Systematically questioning the self-fulfilling prophecy of EEG’s low resolution

Electroencephalography (EEG) has long been believed to be a low-resolution brain imaging modality. There is a pervasive view in the community of EEG users that increasing the number of EEG electrodes will not improve imaging resolution. We have worked towards systematically understanding the reasons this view exists, and towards changing perceptions about EEG.

The heart of our argument is explained in our recent paper titled “An Information-theoretic View of EEG Sensing”. We examined previous estimates of the number of sensors needed to recover the EEG signal. These estimates relied on computing the “spatial Nyquist rate” of scalp EEG, and it turned out that they underestimated the number of sensors required to reliably recover the continuous scalp potential. We also showed, through simulations and intuitive arguments, that the number of EEG sensors required to recover the signal within the brain could be even higher than the number prescribed by the spatial Nyquist rate.

Naturally, the follow-up question is: “What are the fundamental theoretical limits of the imaging resolution achievable by EEG, and how does this resolution improve with increasing numbers of sensors?” While this paper gives a first-pass at understanding these limits, we followed up with a paper that gave the first analytical results showing that EEG’s imaging resolution could improve with an increase in the number of EEG sensors.

We also gave information-theoretic techniques to help save power in a future implementation of an ultra-high-density EEG system. This last point, which focuses on the practical aspects of instrumenting such a high-density EEG system was first discussed in Allerton 2015, where we introduced a “hierarchical referencing mechanism” to save power and circuit area.

Towards showing experimentally and practically that high density EEG, and specifically, super-Nyquist sampling is more informative than conventional EEG, we recently conducted some experiments in collaboration with Amanda Robinson, Marlene Behrmann and Mike Tarr at CMU’s Psychology department, which show that high-density EEG has better classification accuracy than low-density EEG in classifying between visual stimuli of different spatial frequencies.

 Link to paper  Link to poster

Directions of information flow and Granger Causality

Granger causality is an established measure of the “causal influence” that one statistical process has on another. It has been used extensively in neuroscience to infer statistical causal influences. Recently, however, many works in the neuroscience literature have begun to compare Granger causal influences along forward and reverse links of a feedback network in order to determine the direction of information flow in this network.

Greater GC can be opposite the direction of Info flow

We asked whether comparing Granger causal influences correctly captures the direction of information flow in a simple feedback network. We discovered, using simple theoretical experiments, that comparison of Granger causal influences can, in fact, yield an answer that is opposite to the true direction of information flow.

 Link to paper  Link to poster