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Brain Computer Interface Technologies in the Coming Decades

Brent J. Lance, Scott E. Kerick, Anthony J. Ries, Kelvin S. Oie, Kaleb McDowell

arXiv:1211.0886v1cs.HCcs.ET

TL;DR

BCIs address increasingly complex human-computer interactions by integrating brain-based neurotechnologies with human capabilities and limitations. The paper describes near-term task-oriented applications and a longer-term holistic approach that merges brain, behavioral, task, and environmental information, while recognizing persistent challenges in detecting and interpreting neural signatures.

  • Problem

    Increasingly complex technologies can overwhelm human capabilities for optimal interaction, while BCI development still faces obstacles in detecting and interpreting neural signatures.

  • Method

    The paper surveys near-term task-oriented BCI applications and proposes a farther-term holistic approach that merges critical brain, behavioral, task, and environmental information.

  • Results

    Projected BCI developments are expected to move brain-based neurotechnologies beyond toys and prototypes toward broader applications across patient populations and human-computer interactions.

  • Takeaways & Limitations

    BCIs may support advanced communication, direct control of prostheses and wheelchairs, and broader task-oriented and opportunistic applications.

  • Takeaways & Limitations

    Low signal-to-noise ratios make enhancing control a nontrivial problem, and detecting and interpreting neural signatures remains a hurdle for BCI development.

Abstract

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As the proliferation of technology dramatically infiltrates all aspects of modern life, in many ways the world is becoming so dynamic and complex that technological capabilities are overwhelming human capabilities to optimally interact with and leverage those technologies. Fortunately, these technological advancements have also driven an explosion of neuroscience research over the past several decades, presenting engineers with a remarkable opportunity to design and develop flexible and adaptive brain-based neurotechnologies that integrate with and capitalize on human capabilities and limitations to improve human-system interactions. Major forerunners of this conception are brain-computer interfaces (BCIs), which to this point have been largely focused on improving the quality of life for particular clinical populations and include, for example, applications for advanced communications with paralyzed or locked in patients as well as the direct control of prostheses and wheelchairs. Near-term applications are envisioned that are primarily task oriented and are targeted to avoid the most difficult obstacles to development. In the farther term, a holistic approach to BCIs will enable a broad range of task-oriented and opportunistic applications by leveraging pervasive technologies and advanced analytical approaches to sense and merge critical brain, behavioral, task, and environmental information. Communications and other applications that are envisioned to be broadly impacted by BCIs are highlighted; however, these represent just a small sample of the potential of these technologies.

1. Introduction

BCIs emerged to address limits in human-computer communication by using brain signals to influence interactions with computers, environments, and people. The paper frames future systems as adaptive technologies that could extend beyond clinical assistance toward individualized training, diagnosis, communication, and everyday interaction.

  • Future BCIs are envisioned for adaptive training and rehabilitation, environmental adjustment, early disease detection, and communication support.
  • BCIs use online brain-signal processing to influence interactions with computers, environments, and other humans.
  • Advances in sensing, neuroscience, analytics, and mobile computing could move BCIs beyond interfaces toward broader human-system interaction.
  • Increasing computational complexity makes limited human-machine communication bandwidth an increasingly important systems constraint.
  • Neuroscience research offers insights into brain states and mental processes that could expand human-computer and human-human interaction.
  • Early BCIs primarily targeted clinical populations, including locked-in patients and direct control of prostheses, wheelchairs, and communication devices.

Early Approaches to BCI

Early BCI applications were constrained by task-specific operation and a focus on users with severely limited communication abilities. They enabled clinically valuable control, but generally remained less effective than conventional interfaces for healthy users.

  • Early BCI applications required users to focus on a particular task, making the interfaces inseparable from the task being performed.
  • Spelling systems detected P300 responses or motor-imagery-related EEG changes to select letters from presented options.
  • Motor-imagery systems used EEG signals from the perceptual-motor system to select letters based on imagined movement.
  • Clinical BCI applications enabled direct control of cursors and communication devices for paralyzed or locked-in patients.
  • Healthy users typically outperform these BCI applications using conventional alternatives such as mice and speech.
  • Newer applications extended BCIs toward emotion-aware entertainment and joint human-computer object detection.

Recent Advancements in Neurotechnologies

Recent advances in sensing, wearable integration, and analytical methods are broadening BCIs from specialized prototypes toward everyday use. These developments support augmented BCIs designed to operate during natural interaction with real-world environments.

  • Advances in brain imaging and sensing are enabling new capabilities and seamless integration into clothing and environmental devices.
  • Augmented BCIs are defined as BCIs usable by individuals in everyday life.
  • Everyday BCIs must function during movement and interaction while providing non-invasive, rapid-setup, stable, robust, comfortable, and durable EEG measurement.
  • Improved algorithms can analyze and interpret brain data gathered in noisy, real-world environments.
  • Projected developments could move neurotechnologies from toys and specialized prototypes to a core technology for everyday human-system interactions.

3. The Promise of Incorporating the Brain

The paper proposes incorporating brain information into adaptive human-system design by combining neural, behavioral, task, and environmental data. It surveys possible applications while emphasizing that reliable individual-level neural decoding remains a central development challenge.

  • Brain activity varies across people and over time, motivating technologies tailored to individual users and current mental states.
  • Tracking neural adaptation online could support new approaches to training, education, and rehabilitation.
  • Combined human-computer systems could integrate operator and computer capabilities, states, goals, actions, task constraints, and environmental information.
  • Near-term applications favor non-invasive methods, but realizing the broader potential depends on reliably extracting accurate, high-resolution neural indices.
  • Neuroimaging can reveal visual processing, timing, spatial location, stimulus properties, category, familiarity, and partial image content.
  • Neural measures also relate to memory, motor planning, deception, decision making, effort, fatigue, emotion, stress, and cognitive load.
  • Established group-level, correlation-based neuroscience techniques will generally be unsuccessful across the broad BCI applications envisioned.

4. The Future of BCI Technologies

Future BCIs are expected to move from task-oriented applications toward systems that combine brain, behavioral, task, and environmental information. Longer-term opportunistic applications could support daily-life, medical, educational, work, and social uses, but near-term development is constrained.

  • Task-oriented BCIs: Task-oriented BCIs use application context to interpret neural signals rather than seeking indices that generalize across tasks.Access to information about what the user is doing is expected to improve interpretation of incoming neural data.
  • Task-oriented BCIs: Future task-oriented BCIs are expected to combine improved sensors, analysis algorithms, artificial intelligence, pervasive technologies, and computing algorithms.These systems are expected to collect and analyze brain data over extended periods and become prevalent in daily life.
  • Opportunistic BCIs: Opportunistic BCIs could provide benefits without directly supporting the task being performed or requiring additional overhead once users regularly wear brain sensors.Examples include adapting local environments, supporting mood or mental state, and screening for neural disease indicators.
  • Opportunistic BCIs: Opportunistic applications may use screening results to trigger further analysis, recommend medical diagnosis, or suggest preventative measures.These responses can move the BCI into a task-oriented domain or direct the user toward a doctor.
  • Development and scope: Near-term opportunistic BCI development is limited by operating constraints and will require large-scale, extended data collection and extensive individual customization.Over longer time frames, such systems may have benefits across medical, education, work, and social applications.

Direct Control

Direct-control BCIs aim to use brain signals to control prostheses, wheelchairs, robotic devices, and entertainment systems. Their development is limited by human multitasking and coordination capacities, incomplete neural measurement, low-bandwidth signals, and competition with conventional interfaces.

  • Scope and applications: Direct-control BCIs use brain signals to manipulate objects or devices, with clinical applications including prostheses, wheelchairs, and communication systems.The envisioned applications extend from current clinical uses to consumer and entertainment settings.
  • Human capabilities and limits: Human multitasking and multi-limb coordination limitations constrain the benefits of controlling additional mechanical limbs.The brain’s cortex appears to provide higher-level goals while lower-level mechanisms execute fine limb control.
  • Neural measurement: Current imaging and analysis methods provide only limited glimpses of brain function, often extracting relevant information from a small portion of neural signals.Many recording technologies also do not function in everyday environments and require considerable computational processing.
  • Control effectiveness: Brain-signal direct control for healthy users remains difficult because available technologies provide relatively low bandwidth and low signal-to-noise ratios.The signal-derived information must add value beyond channels such as manual input.
  • Future directions: Near-term direct-control progress is expected mainly in medical prosthesis and wheelchair control, while alternative behavioral or physiological signals may support task-specific control.Entertainment systems may accommodate ineffective movements by limiting possible outcomes, and later systems may coordinate multiple robots.
  • Future directions: Direct-control BCIs will remain in competition with alternative human-computer interfaces.Their success depends on whether neural control performs better overall and imposes lower load on the system and user.

Indirect Control

Indirect-control BCIs can augment human-system control without requiring the user to participate directly in the control task. Their usefulness depends on detecting intent or error signals robustly, specifically, and promptly enough to outperform alternatives.

  • Indirect control: Indirect control uses neural correlates of perceived errors to influence a robotic controller’s manipulation strategy without directly engaging the operator.A robotic arm could use detected error signals to select among alternative ways to manipulate a door handle.
  • Neural signals: Potential inputs include Error-Related Negativity and combinations of signals associated with frustration, attention, engagement, or comprehension.These signals provide information about errors perceived by the human user.
  • Indirect control: Indirect-control applications can augment control systems without requiring users to make corrections directly.The system may respond to the user’s perceived error while the user observes the robot’s action.
  • Design requirements: Success depends on the robustness, specificity, and timeliness of detecting signals that indicate user intent or approaching errors.The neurally based strategy must also perform more effectively than alternatives in overall performance and load on the system and user.

Communications

BCIs may have especially broad effects in communications by integrating neural signals with behavioral and environmental sensing. Proposed systems could increase communication bandwidth, estimate comprehension, adapt information displays, and support idea formation.

  • Communications: Communications is presented as a potentially large area of BCI impact, using a holistic system that combines communication technologies with pervasive sensing and computing.The approach extends beyond speech generation toward passing meaning between people or systems.
  • Scope boundary: Early communication BCIs were designed for clinical populations and may not extend effectively to healthy populations in their entirety.The paper therefore distinguishes established clinical benefits from broader future communication capabilities.
  • Communications: The proposed communication framework has three aims: increase human-computer bandwidth, enhance or predict comprehension in context, and support idea formation.These concepts organize the envisioned communication applications.
  • Context and comprehension: Neural, facial, body, prosodic, eye-tracking, and head-tracking signals could add contextual information about attention, fatigue, arousal, and other user states.Combining these estimates may support probabilistic predictions of information processing and adaptive displays.
  • Context and comprehension: Neural signatures such as N400 could help detect semantic misunderstanding and support prompts to repeat, rephrase, or revise ambiguous communication.Potential applications include peer communication, teaching, public speeches, advertising, and entertainment.
  • Idea formation: BCIs may help computers infer what users are trying to communicate and build individualized semantic lexicons linking words, images, and sounds.Such lexicons could support systems that connect user concepts and potentially return novel ideas.

Brain-Process Modification

Brain-process modification BCIs could help users adjust neural states through neurofeedback and, later, neural stimulation for training, rehabilitation, and related applications. However, neural complexity, individual variability, limited exploration, and uncertain effects across tasks constrain near-term impact.

  • Potential applications: Neurofeedback-based BCIs could help users adjust brain processes and support training, rehabilitation, treatment of affective disorders, or delayed neural degradation.The passages identify training and rehabilitation as especially promising applications, while also discussing depression and age- or ailment-related degradation.
  • Potential applications: Existing research can discriminate between novice and expert brain processes during physically demanding or concentration-intensive tasks.These distinctions could gauge learning status and inform training toward expert-like brain processing.
  • Neural stimulation: Future systems could combine high-resolution neural stimulation with neurofeedback to stimulate or suppress activity in brain regions of interest.The proposed combination is intended to assist users in achieving desired brain processing.
  • Neural stimulation: Neural stimulation might substantially improve neurofeedback-based brain-process modification, but placing users directly into task-appropriate brain states would be further-term and higher-risk.That possibility requires major improvements in neural-state detection and autonomy, alongside strong consideration of the brain’s complexity.
  • Limitations: Complexity, between-person variability, and poorly understood effects on other tasks make optimal individual neural goal-states difficult to determine.Consequently, the paper argues that brain-processing modification BCIs are unlikely to revolutionize training and rehabilitation in the near term.

Mental State Detection

Mental-state detection BCIs could monitor fatigue, attention, arousal, and affect, then combine neural signals with behavioral and task data to adapt training, medical assessment, and user environments. The envisioned benefits include improved learning outcomes, more cost-effective monitoring, and safer performance in vigilance-based tasks.

  • General applications: Mental-state detection targets fatigue, attention, arousal, and affective levels so systems or environments can adapt to the user.The intended consequence is increased joint user-system performance across a wide range of tasks or support for desired mental and emotional states.
  • Safety and vigilance: Fatigue predictors integrated with mitigation techniques are envisioned to decrease the odds of catastrophic driver errors.Similar fatigue- or attention-based systems may generalize to vigilance tasks requiring sustained attention.
  • Training: Training systems could detect fatigue, frustration, confusion, negative affect, low arousal, or learning-related states to personalize instruction and feedback.Near-term systems could combine these detections with behavioral and task-performance data; longer-term systems could adapt in real time using pedagogical theories and user models.
  • Training: Real-time adaptation to the user’s mental state could improve tutoring-system learning outcomes.The proposal extends state detection from identifying impediments to learning toward continuous training-system adjustment.
  • Medical applications: Hospital fMRI-based BCIs could analyze neural states during task performance and alter tasks as diagnoses are eliminated or narrowed.Mobile EEG applications could enable patients to undergo iterative examinations at home, potentially supporting cost-effective systems for broad patient populations.
  • Medical applications: State-based approaches could also be combined with rehabilitation BCIs to enhance performance.This connects mental-state monitoring with medical rehabilitation applications.

Opportunistic State-Based Detection

Opportunistic BCIs would infer brain states during everyday activities and use those signals to adapt environments, support medical monitoring, and provide personalized assistance. The paper envisions benefits for healthy and clinical populations, while noting that these applications face substantial developmental issues.

  • Everyday adaptation: Office BCIs could detect affective states to modify music and digital-frame images, alter lighting or shades for migraine-related signals, and cue non-work activities during fatigue or stress.The figure describes pervasive sensors and devices supporting online brain-state detection and human-computer interaction.
  • Sleep applications: Sleep-related BCIs could wake users during an optimal sleep phase and use sleep monitoring or calendar information to suggest sleep times and alarms.The proposed goal is more refreshed and alert awakening, with possible memory-related applications through monitoring rapid eye movement sleep.
  • Medical monitoring: Opportunistic BCIs could use pervasive technologies to monitor neural states during daily living rather than relying only on scheduled medical examinations.This could enable minimally invasive testing, more frequent monitoring, and earlier detection of slowly developing neural pathologies.
  • Medical monitoring: Pervasive brain monitoring could support at-home care and independent living for clinical or elderly populations.Monitoring clinically relevant symptoms could be coupled with automated remote treatment to minimize or prevent harmful conditions such as epileptic seizures.
  • Assistive agents: Task-oriented and opportunistic monitoring could be integrated with virtual medical agents for periodic rehabilitation treatments, evaluation tasks, and real-world progress monitoring.Stroke rehabilitation is given as an example of combining structured tasks with day-to-day observation.
  • Scope and outlook: Opportunistic state-based applications could improve human-system task performance and provide assistive medical applications for healthy and clinical populations.The paper links these benefits to integration with pervasive computing and assistive-agent technologies, while acknowledging developmental issues.

5. Conclusions

The paper envisions BCIs evolving from near-term, individualized task-oriented applications toward holistic systems that merge brain, behavioral, task, and environmental information. Their broader potential depends on overcoming variability, noise, and the difficulty of interpreting neural signatures in unconstrained settings.

  • Current neuroscience and neurotechnology advances create opportunities for BCIs to improve human-computer interactions and provide computers with predictive information about users’ cognitive and emotional states.The paper identifies this capability as potentially transformative for interfaces and everyday interactions.
  • BCI development must overcome difficulties caused by dynamic environments, environmental noise, overlapping neural processes, and changes in neural signatures over time.The paper presents these obstacles as central constraints on interpreting neural processes and behavior at any given time.
  • Near-term BCIs should target task-specific signals that are difficult to obtain otherwise, tolerate imperfect performance, and emphasize application-specific outcomes over abstract constructs.The paper contrasts predicting task-specific performance declines with attempting to predict general fatigue.
  • Near-term applications are more likely to succeed when calibrated to individual users or based on individual classification rather than designed to generalize across broad or normative populations.This recommendation responds to substantial variation in neural signals across and within individuals.
  • Far-term BCIs may merge brain, behavioral, task, and environmental data using pervasive sensing, advanced analytics, and computational infrastructure such as cloud extensions.The envisioned holistic approach also explores synergies between humans and computers and integrates brain-function and brain-structure data across multiple scales.
  • This integrated data could support individualized BCIs and a broad range of opportunistic applications that influence daily quality of life.The paper describes these applications as potential outcomes if neural signatures can be detected and interpreted in relatively unconstrained settings.
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