Do Weak Brain Signals Get Amplified When Strong Brain Signals are Evoked?

Brain-computer interfaces (BCIs) facilitate an unprecedented fusion between the human mind and pervasive computing systems, enabling users to engage with connected devices in their environment through neural signaling. Despite their potential, BCIs face certain challenges that hinder their widespread proliferation, such as low SNR and high noise levels in brain signals recorded via non-invasive techniques like EEG, high variability in signals among users that hinders generalization, usability challenges, etc. While brain signals like the error potential (ErrP) showcase low SNR and have lower detection accuracy, there are other kinds of signals that showcase high detection rates and resilience to noise. Motivated by this disparity, we ask ourselves if the abstract cognitive states involved in the evocation of such resilient signals be leveraged to amplify or augment the weaker signals, and thus provide them a performance boost. We investigate this hypothesis by designing an experiment to interface these two kinds of signals and collect EEG data in our lab through human trials. We evaluate our hypothesis and contrast our results with other datasets of isolated signals using spatial filtering and deep learning models. We obtain negative results and reflect on the insights and the lessons learned based on them and talk about plausible explanations and future work while also reassessing our initial hypothesis.