Neurotechnologies combine neuroscience and engineering to create tools for studying,
repairing, and enhancing brain function. Traditionally, researchers have used neurotechnologies,
such as Brain-Computer Interfaces (BCIs), as assistive devices, for example to allow
locked-in patients to communicate. In the last few decades, non-invasive brain imaging
devices, such as electroencephalography (EEG) and functional near-infrared spectroscopy
(fNIRS), have become more portable and inexpensive, paving the way to innovative applications
of neurotechnologies (Ayaz and Dehais, 2018). Recent trends in neuroergonomics and
neural engineering have used neurotechnologies to enhance various human capabilities,
including (but not limited to) communication, emotion, perception, memory, attention,
engagement, situation awareness, problem-solving, and decision making (Cinel et al.,
2019; Kosmyna and Maes, 2019).
This Research Topic provides a collection of 12 contributions on recent advances in
the development of non-invasive BCIs for human augmentation, with a particular emphasis
on brain stimulation and neural decoding.
To introduce the topic of human augmentation, Dehais and colleagues propose a two-dimensional
framework that incorporates arousal and task engagement to characterize different
variables typically used in human augmentation, such as mental workload and human
performance (Dehais et al., 2020). Specifically, poor task engagement leads to mind
wandering or effort withdrawal depending on arousal level, while a too high arousal
could lead to perseveration or in attentional blindness and deafness. Neurotechnologies
could, therefore, be used to guide the brain to an optimal position in the arousal-engagement
space to maximize performance, a position characterized by medium levels of arousal
and high task engagement, which could be achieved, for example, by using brain stimulation
or neurofeedback.
A few studies in this Research Topic investigated the use of non-invasive brain stimulation
to augment human performance: a very popular topic in the area of neurotechnologies
(Kadosh, 2014; Santarnecchi et al., 2015). Pilly and colleagues propose a novel paradigm
based on virtual reality to use transcranial electrical stimulation (tES) to extend
long-term metamemory (Pilly et al.). By applying periodic brief pulses while participants
were asleep, they improved memory recall of one-shot viewing of naturalistic episodes
over 48 h by 10–20%. Patel and colleagues performed a systematic meta-analysis to
review the use of transcranial direct-current stimulation (tDCS) for improving motor
performance in upper limbs (Patel et al.). Brain stimulation significantly reduces
reaction time, task execution time, and increases force and accuracy in elbow flexion
tasks. Wang and colleagues reported that combining brain stimulation with physical
training increases motor-evoked potential (MEP) amplitude and muscle strength, and
decreases the dynamic posture stability index, reaction time, and error rate in motor
learning tasks (Wang et al.). Similarly, Hollis and colleagues explored the use of
transcranial static magnetic field stimulation (tSMS) to facilitate motor learning
in healthy children. They found that tSMS did not increase MEP amplitude in children
(as found by Wang and colleagues in adults), suggesting that age is a critical factor
for the effectiveness of brain stimulation. Yet, they found tSMS inhibited early motor
learning and facilitated later stage motor learning in the non-dominant hand, which
motivated future investigations of tSMS as a potential non-invasive therapy for children
with cerebral palsy (Hollis et al.).
Another set of studies focused on using non-invasive neuroimaging to decode specific
mental states, which could provide further insights into brain activity. Asgher and
colleagues used fNIRS and deep learning to estimate four different levels of mental
workload in human participants (Asgher et al.). While traditional machine learning
algorithms reached accuracies below 70%, convolutional neural networks with long short-term
memory layers achieved significantly better performance of almost 90% accuracy across
the four classes. These results exemplify the potential of deep learning in neural
decoding for human augmentation. In another contribution, Klaproth and colleagues
used passive BCIs to track perception and auditory processing of pilots during operations
(Klaproth et al.). In particular, they found that a passive BCI could use EEG to distinguish
between task-relevant and irrelevant alerts received by the pilot, hence improving
situation awareness. This work demonstrates how passive BCIs could work as monitoring
devices in a practical scenario without disrupting the main task.
Another neural decoding problem with direct applications in BCI research is mental
imagery. Wairagkar and colleagues showed that temporal patterns extracted from EEG
activity are sufficient to achieve single-trial classification of five different mental
imagery tasks (Wairagkar et al.). These patterns can, therefore, be used as control
signals of non-invasive BCIs, which could translate them into commands for external
devices. Also in the area of neural decoding, Li and colleagues have shown the possibility
of using advanced machine learning and signal processing techniques to decode emotions
from EEG signals (Li et al.). In this domain, other work has tackled this challenge
using more invasive recordings (Sani et al., 2018). Yet, to enable broadly-applicable
human augmentation, similar results have to be achieved with non-invasive devices,
such as the EEG used by Li and colleagues, which pose fewer ethical and socio-economic
barriers than invasive devices.
Another study tackles the exciting area of speech decoding, which aims at translating
brain activity into meaningful speech. This problem has been extensively tackled using
invasive recordings, such as electrocorticography (Herff et al., 2015; Herff and Schultz,
2016; Angrick et al., 2019; Anumanchipalli et al., 2019). Here, Dash and colleagues
demonstrated that this is possible even with non-invasive and, therefore, more practical
neural recording devices, such as MEG (Dash et al.).
The transition to non-invasive, real-world BCIs for human augmentation would require
strategies to enhance the limited signal quality recorded from the brain. As such,
multimodal BCIs depending on a combination of physiological signals will be increasingly
important. In that domain, Stuldreher and colleagues determined the synchrony between
EEG, heart rate, and electrodermal activity while participants were engaged in an
auditory task (Stuldreher et al.). They found that each modality works well in certain
scenarios, and that merging all modalities into a unique metric seems most robust
across a broad range of applications.
Finally, the development of new non-invasive neurotechnologies presents many opportunities
for clinical and field applications as well as multifaceted new challenges (Dehais
et al., 2020). In a review paper of this Research Topic, Gaudry and colleagues delve
into the neuroethical issues that we might face in the upcoming decades as neurotechnologies
transition from research to practice, and even home and office settings (Gaudry et
al.).
We hope this Research Topic provides the reader with updates on recent advances in
the area of non-invasive neurotechnologies for human augmentation. We would like to
thank all authors who contributed, the reviewers who provided invaluable and timely
feedback to the authors, and Dr. Eleonora Adami for designing the cover picture of
this Research Topic.
Author Contributions
DV wrote the first draft of the manuscript. All authors contributed to manuscript
revision, read, and approved the submitted version.
Conflict of Interest
DV is an employee of Neurable Inc. The remaining authors declare that the research
was conducted in the absence of any commercial or financial relationships that could
be construed as a potential conflict of interest.
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