Recent advances in neurotechnology allow for an increasingly tight integration of the human brain and mind with artificial cognitive systems, blending persons with technologies and creating an assemblage that we call a hybrid mind. In some ways the mind has always been a hybrid, emerging from the interaction of biology, culture (including technological artifacts) and the natural environment. However, with the emergence of neurotechnologies enabling bidirectional flows of information between the brain and AI-enabled devices, integrated into mutually adaptive assemblages, we have arrived at a point where the specific examination of this new instantiation of the hybrid mind is essential. Among the critical questions raised by this development are the effects of these devices on the user’s perception of the self, and on the user’s experience of their own mental contents. Questions arise related to the boundaries of the mind and body and whether the hardware and software that are functionally integrated with the body and mind are to be viewed as parts of the person or separate artifacts subject to different legal treatment. Other questions relate to how to attribute responsibility for actions taken as a result of the operations of a hybrid mind, as well as how to settle questions of the privacy and security of information generated and retained within a hybrid mind.
Objective: Transcranial direct current stimulation (tDCS) improves motor learning and can influence emotional processing or attention. However, it remained unclear whether learned electroencephalography (EEG)-based brain-machine interface (BMI) control during tDCS is feasible and how application of transcranial electric currents during BMI control would interfere with feature-extraction of physiological brain signals. Here we tested this combination and evaluated stimulation-dependent artifacts across different EEG frequencies and stability of motor imagery-based BMI control. Approach: Ten healthy volunteers were invited to two BMI-sessions, each comprising two 60-trial blocks. During the trials, modulation of mu-rhythms (8-15Hz) associated with motor imagery recorded over C4 was translated into online cursor movements on a computer screen. During block 2, either sham (session A) or anodal tDCS (session B) was applied at 1mA with the stimulation electrode placed 1cm anterior of C4. Main results: tDCS was associated with a significant signal power increase in the lower frequencies most evident in the signal spectrum of the EEG channel closest to the stimulation electrode. Stimulation-dependent signal power increase exhibited a decay of 12dB per decade, leaving frequencies above 9Hz unaffected. Analysis of BMI control performance did not indicate a difference between blocks and tDCS conditions. Conclusion: Application of tDCS during learned EEG-based self-regulation of brain oscillations above 9Hz is feasible and safe, and might improve applicability of BMI systems in patient populations.
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