Spelling interface using intracortical signals in a completely locked-in patient enabled via auditory neurofeedback training

Chaudhary, Ujwal; Vlachos, Ioannis; Zimmermann, Jonas B.; Espinosa, Arnau; Tonin, Alessandro; Jaramillo-Gonzalez, Andres; Khalili-Ardali, Majid; Topka, Helge; Lehmberg, Jens; Friehs, Gerhard; Woodtli, A.; Donoghue, John P.; Birbaumer, Niels

doi:10.1038/s41467-022-28859-8

Cited by 73 publications

(50 citation statements)

References 40 publications

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“…We even rely on conscious reports to assess our own conscious state, e.g., during dreams, anesthesia or under drugs. Moreover, the absence of a conscious report from others (for example in disorders of consciousness, or in other species) or ourselves (e.g., during NREM sleep) is often mistakenly conflated as evidence of lack of experience that we extrapolate to other living [Chaudhary et al, 2022] and non-living agents.…”

Section: Neurophenomenology: Probing the Structure Of Experiencementioning

confidence: 99%

AIT Foundations of Structured Experience

Ruffini¹,

Lopez-Sola²

2022

J. AI. Consci.

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In this paper, we present a unifying framework to study consciousness based on algorithmic information theory (AIT). We take as a premise that “there is experience” and focus on the requirements for structured experience ([Formula: see text]) — the spatial, temporal, and conceptual organization of our first-person experience of the world and of ourselves as agents in it. Our starting point is the insight that access to good models — succinct and accurate generative programs of world data — is crucial for homeostasis and survival. We hypothesize that the successful comparison of such models with data provides the structure to experience. Building on the concept of Kolmogorov complexity, we can associate the qualitative aspects of [Formula: see text] with the algorithmic features of the model, including its length, which reflects the structure discovered in the data. Moreover, a modeling system tracking structured data will display dimensionality reduction and criticality features that can be used empirically to quantify the structure of the program run by the agent. KT provides a consistent framework to define the concepts of life and agent and allows for the comparison between artificial agents and [Formula: see text]-reporting humans to provide an educated guess about agent experience. A first challenge is to show that a human agent has [Formula: see text] to the extent they run encompassing and compressive models tracking world data. For this, we propose to study the relation between the structure of neurophenomenological, physiological, and behavioral data. The second is to endow artificial agents with the means to discover good models and study their internal states and behavior. We relate the algorithmic framework to other theories of consciousness and discuss some of its epistemological, philosophical, and ethical aspects.

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Section: Neurophenomenology: Probing the Structure Of Experiencementioning

confidence: 99%

AIT Foundations of Structured Experience

Ruffini¹,

Lopez-Sola²

2022

J. AI. Consci.

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“…Reliable communication channel: Although implanted communication-BCIs are relevant for those who have lost all muscle-based communication, 15 there are important advantages to enrolling participants with progressive diseases such as ALS in research before this stage is reached, including the opportunity to obtain first-person consent, an assessment of cognitive functioning in advance of the surgery (see below) and feedback on, for example, user satisfaction, fatigue, and motivation (see below) throughout the study. Therefore, we advise that candidates should have at least one reliable channel to answer closed (yes/no) questions, for example, by using eye blinks.…”

Section: What Eligibility Criteria Should Be Considered and How Shoul...mentioning

confidence: 99%

Methodological Recommendations for Studies on the Daily Life Implementation of Implantable Communication-Brain–Computer Interfaces for Individuals With Locked-in Syndrome

Vansteensel

Branco

Leinders

et al. 2022

Neurorehabil Neural Repair

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Implantable brain–computer interfaces (BCIs) promise to be a viable means to restore communication in individuals with locked-in syndrome (LIS). In 2016, we presented the world-first fully implantable BCI system that uses subdural electrocorticography electrodes to record brain signals and a subcutaneous amplifier to transmit the signals to the outside world, and that enabled an individual with LIS to communicate via a tablet computer by selecting icons in spelling software. For future clinical implementation of implantable communication-BCIs, however, much work is still needed, for example, to validate these systems in daily life settings with more participants, and to improve the speed of communication. We believe the design and execution of future studies on these and other topics may benefit from the experience we have gained. Therefore, based on relevant literature and our own experiences, we here provide an overview of procedures, as well as recommendations, for recruitment, screening, inclusion, imaging, hospital admission, implantation, training, and support of participants with LIS, for studies on daily life implementation of implantable communication-BCIs. With this article, we not only aim to inform the BCI community about important topics of concern, but also hope to contribute to improved methodological standardization of implantable BCI research.

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“…It is unknown if the neural signal in participants that cannot (fully) move anymore is similar enough to decode as well as in epilepsy patients. However, successful studies in paralyzed patients indicates that motor activity can still be decoded [9,10]. Lastly, we did not objectively control for movements using EMG electrodes during the imagined movement task.…”

Section: Limitationsmentioning

confidence: 99%

“…For a user to achieve sufficient control, the decoder that translates user intent to device motor output needs to have access to rich movement-related neural information. Traditionally, the primary motor cortex has been the primary target, as it has direct downstream output to muscle actuators [7][8][9][10]. For example, implantations of microelectrode arrays (MEA) in the hand-knob area of the human primary motor cortex have resulted in state-of-the-art decoders that can decode imagined handwriting at speeds comparable to regular smartphone typing [11].…”

Section: Introductionmentioning

confidence: 99%

Executed and imagined grasping movements can be decoded from lower dimensional representation of distributed non-motor brain areas

Ottenhoff

Verwoert

Colon

et al. 2022

Preprint

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Using brain activity directly as input for assistive tool control can circumvent muscular dysfunction and increase functional independence for physically impaired people. Most invasive motor decoding studies focus on decoding neural signals from the primary motor cortex, which provides a rich but superficial and spatially local signal. Initial non-primary motor cortex decoding endeavors have used distributed recordings to demonstrate decoding of motor activity by grouping electrodes in mesoscale brain regions. While these studies show that there is relevant and decodable movement related information outside the primary motor cortex, these methods are still exclusionary to other mesoscale areas, and do not capture the full informational content of the motor system. In this work, we recorded intracranial EEG of 8 epilepsy patients, including all electrode contacts except those contacts in or adjacent to the central sulcus. We show that executed and imagined movements can be decoded from non-motor areas; combining all non-motor contacts into a lower dimensional representation provides enough information for a Riemannian decoder to reach an area under the curve of 0.83 ± 0.11. Additionally, by training our decoder on executed and testing on imagined movements, we demonstrate that between these two conditions there exists shared distributed information in the beta frequency range. By combining relevant information from all areas into a lower dimensional representation, the decoder was able to achieve high decoding results without information from the primary motor cortex. This representation makes the decoder more robust to perturbations, signal non-stationarities and neural tissue degradation. Our results indicate to look beyond the motor cortex and open up the way towards more robust and more versatile brain-computer interfaces.

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Spelling interface using intracortical signals in a completely locked-in patient enabled via auditory neurofeedback training

Cited by 73 publications

References 40 publications

AIT Foundations of Structured Experience

AIT Foundations of Structured Experience

Methodological Recommendations for Studies on the Daily Life Implementation of Implantable Communication-Brain–Computer Interfaces for Individuals With Locked-in Syndrome

Executed and imagined grasping movements can be decoded from lower dimensional representation of distributed non-motor brain areas

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