In his recent talk, Joe Vukov explored brain-computer interfaces and their ethical implications, focusing mainly on issues of autonomy and privacy. He discussed a study by Hemmings Wu et al. on a closed-loop brain-computer interface (BCI) designed to treat loss of control eating in patients for whom previous treatment had been unsuccessful. The system detects neural signals associated with unhealthy eating behaviors originating in the nucleus accumbens and responds by delivering deep brain stimulation via the RNS System, preventing these impulses from developing into physical actions. As a professor of philosophy, Vukov’s main concern was with the autonomy and privacy retained by patients who opted in to treatments such as these. Because the system operates in a closed loop, its effects are not externally visible, making it difficult for patients to monitor, evaluate, or end the treatment once it begins. As Vukov discussed, this type of automated control risks limiting a patient’s ability to consent in real time or withdraw from treatment, drawing attention to the decrease in direct control that users will have as BCIs become more effective. He contrasts this with open-loop systems that translate the neural signals into external actions, such as speech or movement of a limb, introducing the concern that private thoughts could be continuously monitored or exposed without sufficient user control.
In a new study, Erin M. Kunz et al. investigated the use of BCIs to decode inner speech in individuals with paralysis as a means to restore communication. For this study, four participants from the BrainGate2 trial were recruited, each with varying abilities to produce speech or communicate with others. In order to study the neural representations of their speech, microelectrode arrays were placed in the precentral gyrus of each participant. This area of the motor cortex produces neural activity associated with inner speech, perceived speech, and reading. After performing various tasks, some of the participants’ results showed a decoding accuracy for inner and perceived speech that was the same or better than attempted speech. It was also found that the neural representations of attempted and inner speech overlapped, showing a correlation in neural firing rates. This showed that words are encoded similarly across different behaviors, but also raises the possibility of unintentionally decoding the private inner speech of users. To address this concern, the researchers shifted focus to the difference in motor-intent signal between inner and attempted speech, which allowed decoders to distinguish between the behaviors and more accurately transcribe the desired speech. Another proposed solution to prevent unwanted decoding of inner speech is to employ a user-controlled keyword that could be said by users to “lock” and “unlock” the decoders, allowing inner speech to continue without being expressed out loud.
The work done by Wu et al. and Kunz et al. highlight a promising direction for BCIs to continue making meaningful impacts on patients’ lives, while still preserving their privacy. Vukov’s discussion of BCIs show that systems that work independently of user acknowledgement risk diminishing user autonomy, regardless of therapeutic benefits. The research done by Kunz et al., however, demonstrates that it is possible for BCIs to be designed with user intention and control as a priority. By incorporating preventative measures such as motor-intent discernment or user-activated keywords, boundaries between private thoughts and deliberate speech can be established. These measures address the concerns raised by Vukov, suggesting that the ethical risks of BCIs are not necessarily a symptom of the technology, but rather, dependent on the design and implementation of each treatment system. Ensuring that users retain control over when and how their neural activity is interpreted will be essential for the further development and use of BCIs.
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