Past BCI Thursdays recordings

Novel online event mixing Industry and Academia talks.

Novel online event mixing Industry and Academia talks.

Careers within the brain-computer interface (BCI) community can span a variety of organizations including academia, industry, or government. However, how to successfully navigate careers between different organizations is an important topic that is not frequently discussed. In this career panel, three panelists in various roles (academia, industry, and government) within the BCI community will discuss their career paths which have included transitioning between roles, such as between academia to industry or vice versa. The goal of the career panel is for trainees to hear and to interact with prominent BCI scientists to understand various roles within the BCI community, how to transition between them, and lessons learned.

Design considerations for closed-loop decoder adaptation algorithms in invasive BCIs
Closed-loop decoder adaptation (CLDA) is a powerful technique in invasive brain-computer interfaces. CLDA is frequently used to train and optimize decoding algorithms “in situ,” reducing errors in performance that arise due to differences between open loop decoder training and closed loop BCI operation. CLDA has also proven useful as a strategy to maintain performance over time despite non-stationary measurements (e.g. signal drift). Many design choices must be made when creating a CLDA algorithm, which will influence its overall performance and utility. These choices include the form of error signal used to guide algorithm retraining, the learning rules used to update the decoder, the timescale of decoder updates, and the decoder parameters to update. User-decoder interactions must also be considered when designing CLDA algorithms. In this talk, I will briefly survey these details. I will present a case-study of CLDA algorithms optimized to robustly and rapidly initialize a BCI decoder independent of initialization, and review the algorithm design choices that led to the success of these algorithms. I will also briefly touch on the use of CLDA for promoting co-adaptation between the brain and decoder.

Recorded brain activity displays complex non-linear dynamics and is highly participant and even session dependent. Furthermore, the signals often display many non-stationarities. To still make sense of these challenging data, specific consideration for the machine learning approach must be made.  In this didactic session, common challenges for machine learning in BCI research will be highlighted and some key considerations of how to address them will be discussed. Briefly, both a regression and a classification problem will be discussed. Quick examples, using both invasive and non-invasive data, will be used to illustrate problems and common fallacies. As in many time series data, specific attention must be paid to the evaluation in BCI research, as the strong autocorrelations of the data can otherwise induce exaggerated results. Finally, some key considerations for the translation from offline results to viable BCI solutions will be presented.