Optimizing Computer–Brain Interface Parameters for Non-invasive Brain-to-Brain Interface

Abstract

A non-invasive, brain-to-brain interface (BBI) requires precision neuromodulation and high temporal resolution as well as portability to increase accessibility. A BBI is a combination of the brain-computer interface (BCI) and the computer-brain interface (CBI). The optimization of BCI parameters has been extensively researched, but CBI has not. Parameters taken from the BCI and CBI literature were used to simulate a two-class medical monitoring BBI system under a wide range of conditions. BBI function was assessed using the information transfer rate (ITR), measured in bits per trial and bits per minute. The BBI ITR was a function of classifier accuracy, window update rate, system latency, stimulation failure rate (SFR), and timeout threshold. The BCI parameters, including window length, update rate, and classifier accuracy, were kept constant to investigate the effects of varying the CBI parameters, including system latency, SFR, and timeout threshold. Based on passively monitoring BCI parameters, a base ITR of 1 bit/trial was used. The optimal latency was found to be 100 ms or less, with a threshold no more than twice its value. With the optimal latency and timeout parameters, the system was able to maintain near-maximum efficiency, even with a 25% SFR. When the CBI and BCI parameters are compared, the CBI's system latency and timeout threshold should be reflected in the BCI's update rate. This would maximize the number of trials, even at a high SFR. These findings suggested that a higher number of trials per minute optimizes the ITR of a non-invasive BBI. The delays innate to each BCI protocol and CBI stimulation method must also be accounted for. The high latencies in each are the primary constraints of non-invasive BBI for the foreseeable future.

Evaluation of an English language phoneme-based imagined speech brain computer interface with low-cost electroencephalography

“…ITR has been calculated in prior comparable BCIs ( LaRocco and Paeng, 2020 ), and the ITR was calculated for a phoneme-based EEG BCI. The information transfer rate for SVM and LDA was 5.07 bits per trial, yielding a rate of 304.15 bits per minute.…”

“…In the implemented BCI, the value for is equal to the number of phonemes, 44. Although a low ITR can have direct applications, the maximum ITR of each system configuration was calculated to model the highest possible performance ( LaRocco and Paeng, 2020 ). To simplify the calculation, a sampling window of 1 s was assumed, following the data acquisition protocol.…”

Training protocol for probabilistic Pavlovian conditioning in mice using an open-source head-fixed setup

“…( Hangya et al., 2015 ; Laszlovszky et al., 2020 )). If combined with optogenetic tagging ( Lima et al., 2009 ; Kvitsiani et al., 2013 ; Hangya et al., 2015 ; Széll et al., 2020 ), turn on the laser. Make sure that the water tank used for reward is filled.…”

Applying antagonistic activation pattern to the single-trial classification of mental arithmetic

“…The advantages are low demanding conditions for the surgery and a reduced risk of infection [ 2 , 3 , 4 , 5 ]. However, invasive BCIs techniques still face significant challenges for subjects compared to non-invasive techniques [ 6 , 7 , 8 ]. Non-invasive BCIs are free of surgical risks, medical limitations.…”