Brendan Z Allison

• PhD
• Researcher at University of California, San Diego

This chapter and book present the projects that were nominated for the Annual BCI Research Award in 2023. Brain-computer interfaces (BCIs) are devices that convert direct measures of brain activity into messages or commands. Thus, people can convey information without moving. The Annual BCI Research Awards have identified and publicly recognized th...

The 2023 BCI Research Awards celebrated groundbreaking advancements in brain-computer interfaces (BCIs) with a virtual ceremony held as part of the IEEE Systems, Man, and Cybernetics conference. The awards recognized three innovative projects: a high-performance speech neuroprosthesis, a cutting-edge intracortical speech BCI, and a novel multichann...

Online adaptive canonical correction analysis (OACCA) has been applied successfully in the recently popular steady-state visual evoked potential (SSVEP) target recognition methods. However, due to the significant amount of spatio-temporal relevant background noise in the online historical sample label data of OACCA, there is redundant noise compone...

In steady-state visual evoked potential (SSVEP)based brain-computer interfaces (BCIs), various spatial filtering methods based on individual calibration data have been proposed to alleviate the interference of spontaneous activities in SSVEP signals for enhancing the SSVEP detection performance. However, the necessary calibration procedures take ti...

People with severe brain damage can suffer from a disorder of consciousness (DoC) such as a coma. They may be unconscious for decades, with little or no awareness of themselves or their surroundings, or they might emerge slowly into consciousness. People with DoCs usually cannot control any of their movements to respond to questions, so diagnosing...

The use of Brain–Computer Interfaces (BCI) as rehabilitation tools for chronically ill neurological patients has become more widespread. BCIs combined with other techniques allow the user to restore neurological function by inducing neuroplasticity through real-time detection of motor-imagery (MI) as patients perform therapy tasks. Twenty-five stro...

[This corrects the article DOI: 10.3389/fnins.2020.00582.].

Objective Clinical assessment of consciousness relies on behavioural assessments, which have several limitations. Hence, disorder of consciousness (DOC) patients are often misdiagnosed. In this work, we aimed to compare the repetitive assessment of consciousness performed with a clinical behavioural and a Brain-Computer Interface (BCI) approach. M...

Many people who have had a stroke have trouble moving, even after therapy with the best experts and methods. New ways to make stroke therapy more effective could help people recover more effectively. Some research groups have developed brain-computer interface (BCI) systems that can measure when a stroke patient imagines hand movement by recording...

Disorders of consciousness include coma, unresponsive wakefulness syndrome (also known as vegetative state), and minimally conscious state. Neurobehavioral scales such as coma recovery scale-revised are the gold standard for disorder of consciousness assessment. Brain-computer interfaces have been emerging as an alternative tool for these patients....

We began this book with an introductory chapter so readers could understand more about BCIs and the annual procedures we follow to develop the awards and these books. The subsequent chapters of this book each presented a BCI project that was nominated for a BCI Research Award, with seven project summaries and four interviews. In the concluding chap...

Brain-computer interfaces (BCIs) enable users to send messages or commands directly through brain activity, without any movement. Most BCI systems aim to help persons with serious movement disabilities, but BCIs for consumer applications are increasingly prevalent. Each year since 2010, teams submitted their BCI projects to the BCI Research Awards,...

Brain-computer interface (BCI) systems can provide communication and control without any physical movement. The BCI Research Awards are annual events to select the best BCI projects that year. Groups from around the world submit projects that are scored by a jury of international experts that selects twelve nominees and three winners. We also produ...

The introduction chapter of this book described the BCI Research Awards, selection criteria, nominees, and jury. Developing a good submission for a BCI Research Award is a formidable goal, and being nominated is even more demanding. This book has presented thirteen chapters by the authors of projects nominated for a BCI Research Award in 2019. Some...

Introduction Numerous recent publications have explored Brain Computer Interfaces (BCI) systems as rehabilitation tools to help subacute and chronic stroke patients recover upper extremity movement. Recent work has shown that BCI therapy can lead to better outcomes than conventional therapy. BCI combined with other techniques such as Functional Ele...

The preceding nine chapters in this book presented an introduction and summaries of eight projects that were nominated for a BCI Research Award in 2018. In this chapter, we summarize the 2018 Awards Ceremony where we announced the three winning projects. We interviewed authors of these winning projects – Drs. Ajiboye, Tangermann, and Herff – and th...

Introduction Recent studies explored promising new quantitative methods to analyze electroencephalography (EEG) signals. This paper analyzes the correlation of two EEG parameters, Brain Symmetry Index (BSI) and Laterality Coefficient (LC), with established functional scales for the stroke assessment. Methods Thirty-two healthy subjects and thirty-...

Brain–computer interfaces (BCIs) directly measure brain activity with no physical movement and translate the neural signals into messages. BCIs that employ the P300 event-related brain potential often have used the visual modality. The end user is presented with flashing stimuli that indicate selections for communication, control, or both. Counting...

Persons diagnosed with disorders of consciousness (DOC) typically suffer from motor and cognitive disabilities. Recent research has shown that non-invasive brain-computer interface (BCI) technology could help assess these patients’ cognitive functions and command following abilities. 20 DOC patients participated in the study and performed 10 vibro-...

Many studies reported that ERP-based BCIs can provide communication for some people with amyotrophic lateral sclerosis (ALS). ERP-based BCIs often present characters within a matrix that occupies the center of the visual field. However, several studies have identified some concerns with the matrix-based approach. This approach may lead to fatigue a...

This book reviews the Seventh Annual BCI Research Award, with chapters that review the most promising new BCI research. As with prior years, we announced the first, second, and third place winners as part of a major international BCI conference. The Gala Awards ceremony for the 2017 BCI Research Award was part of the Seventh International BCI Confe...

BCI hackathons are fun, collaborative activities during which teams develop and implement new BCI designs and projects. BCI hackathons have often involved artistic expression, and have led to new headwear designs and BCI systems that let users paint, make music, or play games via thought alone. In the past few years, the number of BCI hackathons wo...

Each year, the Annual BCI Research Award recognizes the top new projects in brain-computer interface (BCI) research. This book contains summaries of these projects from the 2017 BCI Research Award. Each chapter is written by the group that submitted the BCI project that was nominated, and introduction and discussion chapters provide supporting info...

Brain-computer interfaces (BCIs) based on motor imagery have been gaining attention as tools to facilitate recovery from movement disorders resulting from stroke or other causes. These BCIs can detect imagined movements that are typically required within conventional rehabilitation therapy. This information about the timing, intensity, and location...

Persons diagnosed with disorders of consciousness (DOC) typically suffer from motor disablities, and thus assessing their spared cognitive abilities can be difficult. Recent research from several groups has shown that non-invasive brain-computer interface (BCI) technology can provide assessments of these patients' cognitive function that can supple...

Many patients with locked-in syndrome (LIS) or complete locked-in syndrome (CLIS) also need brain-computer interface (BCI) platforms that do not rely on visual stimuli and are easy to use. We investigate command following and communication functions of mindBEAGLE with 9 LIS, 3 CLIS patients and three healthy controls. This tests were done with vibr...

Brain-computer interface (BCI) technology has recently been extended to help patients with disorders of consciousness (DOC) and stroke. These two promising new directions focus on new patient groups and new applications for these groups. First, patients diagnosed with a DOC might benefit from new BCI-based systems that can help assess (or reassess)...

The optimal number of EEG channels is a controversial issue for motor imagery based BCIs for stroke rehabilitation. In this study, we compared the BCI performance with 63, 27 and 16 channels of EEG on three stroke patients across 10 to 24 sessions, and demonstrated that the 16 channels montage yields similar classification error (21.3 ± 11.6, 10.5...

This work presents the recoveriX system, a hardware and software platform specially designed for stroke rehabilitation, as well as the preliminary results of testing it within clinical environment. Three patients with motor impairments due to stroke participated to the current study. In every session, the patients had to imagine 120 left and 120 ri...

The previous chapters should help to show the high quality of the nominated projects, and thus the jury had a very difficult task.

Patients diagnosed with complete locked in syndrome (CLIS) or a disorder of consciousness (DOC) have no reliable control of voluntary movements. Hence, assessing their cognitive functions and cognitive awareness can be challenging. The “gold standard” for such assessments relies on behavioral responses, and recent work using different neuroimaging...

In this experiment, we demonstrate a suite of hybrid Brain-Computer Interface (BCI)-based paradigms that are designed for two applications: assessing the level of consciousness of people unable to provide motor response and, in a second stage, establishing a communication channel for these people that enables them to answer questions with either ‘y...

Brain-computer interface (BCI) research has been advancing quickly, and novel directions with both invasive and non-invasive BCIs could help new patient groups. Each year, the annual BCI Research Award recognizes the top projects in BCI research. This book includes chapters that review these different BCI projects, and this chapter presents more in...

Objective: Amyotrophic lateral sclerosis (ALS) is a rare disease, but is also one of the most common motor neuron diseases, and people of all races and ethnic backgrounds are affected. There is currently no cure. Brain computer interfaces (BCIs) can establish a communication channel directly between the brain and an external device by recognizing b...

Conventional therapies do not provide paralyzed patients with closed-loop sensorimotor integration for motor rehabilitation. This work presents the recoveriX system, a hardware and software platform that combines a motor imagery (MI)-based brain-computer interface (BCI), functional electrical stimulation (FES), and visual feedback technologies for...

Stroke is the leading cause of serious and long-term disability worldwide. Stroke survivors may recover some motor function after rehabilitation therapy. Many studies have shown that motor imagery (MI) based brain-computer Interface (BCI) can improve upper limb stroke rehabilitation. However, as stroke patients have suffered neurological damage, th...

Brain-computer interface (BCI) technology is increasingly used to research new methods to provide assessment and communication for patients diagnosed with a disorder of consciousness (DOC). As this technology advances, it could lead to tools that could support clinical diagnoses, provide communication to some persons who cannot otherwise communicat...

Many patients with locked-in syndrome (LIS) or complete locked-in syndrome (CLIS) also need brain-computer interface (BCI) platforms that do not rely on visual stimuli and are easy to use. We investigate command following and communication functions of mindBEAGLE with 9 LIS, 3 CLIS patients and three healthy controls. This tests were done with vibr...

Non-invasive steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) systems offer high bandwidth compared to other BCI types and require only minimal calibration and training. Virtual reality (VR) has been already validated as effective, safe, affordable and motivating feedback modality for BCI experiments. Augmented real...

Methods: To improve the performance of naive subjects during motor imagery, a novel paradigm was presented that would guide naive subjects to modulate brain activity effectively. In this new paradigm, pictures of the left or right hand were used as cues for subjects to finish the motor imagery task. Fourteen healthy subjects (11 male, aged 22-25 y...

Motor imagery (MI) is a mental representation of motor behavior that has been widely used as a control method for a brain-computer interface (BCI), allowing communication for the physically impaired. The performance of MI based BCI mainly depends on the subject's ability to self-modulate EEG signals. Proper training can help naive subjects learn to...

Several studies have explored brain computer interface (BCI) systems based on auditory stimuli, which could help patients with visual impairments. Usability and user satisfaction are important considerations in any BCI. Although background music can influence emotion and performance in other task environments, and many users may wish to listen to m...

Brain-computer interface (BCI) systems have been used primarily to provide communication for persons with severe movement disabilities. This paper presents a new system that extends BCI technology to a new patient group: persons diagnosed with stroke. This system, called recoveriX, is designed to detect changes in motor imagery in real-time to help...

Recent work has sought to extend brain-computer interface (BCI) technology to persons diagnosed with a disorder of consciousness (DOC). This new approach can use real-time measures of brain activity to facilitate assessment of conscious awareness, and potentially provide communication for some users. We present the mindBEAGLE system, a hardware and...

The gaze-independent brain-computer interface (BCI) based on rapid serial visual presentation (RSVP) is an extension of the oddball paradigm, and can facilitate communication for people with severe neuromuscular disorders. Some studies suggested that a face with eyes only (without other facial features) could evoke ERPs as high as a complete face....

In conventional rehabilitation therapy to help persons with stroke recover movement, there is no objective way to evaluate each patient’s motor imagery. Thus, patients may receive rewarding feedback even when they are not complying with the task instructions to imagine specific movements. Paired associative stimulation (PAS) uses brain-computer int...

Conventional therapies do not provide paralyzed patients with closed-loop sensorimotor integration for motor rehabilitation. Paired associative stimulation (PAS) uses brain-computer interface (BCI) technology to monitor patients’ movement imagery in real-time, and utilizes the information to control functional electrical stimulation (FES) and bar f...

Conventional therapies do not provide paralyzed patients with closed-loop sensorimotor integration for motor rehabilitation. Paired associative stimulation (PAS) uses brain-computer interface (BCI) technology to monitor patients’ movement imagery in real-time, and utilizes the information to control functional electrical stimulation (FES) and bar f...

The preceding chapters summarized ten of the most promising BCI projects in 2014. As we said in the introduction, the jury had a difficult time selecting ten nominees—and three winners—out of the 69 submitted projects. 2014 was the first year we chose a second and third place winner, which only added to the competition and the excitement at our Gal...

This book presents the latest research in brain-computer interface (BCI) systems. A BCI is a device that reads voluntary changes in brain activity, then translates these signals into a message or command in real-time. Early BCI work focused on providing communication for persons with severe movement disabilities. These patients have little or no ab...

Most brain–computer interface (BCI) systems utilize one of three approaches: sensorimotor rhythms (SMRs), P300s, or steady-state visually evoked potentials (SSVEPs). Numerous groups have reported that these approaches do not provide effective communication for a small percentage of users. This phenomenon has been called BCI illiteracy, inefficiency...

Brain-Computer Interfaces (BCIs) are devices that can enable communication or control without movement. The BCI detects specific patterns of the user’s brain activity that reflect different messages or commands that the user wants to send, such as spelling or changing a television channel. Signal processing tools then decode this brain activity to...

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative neurological condition categorized as an orphan disease and at present the primary treatment is managing symptoms. It leads to severe paralysis, resulting in the need for the patient to use assistive technologies to support them in their daily activities. When the condition is severe, mains...

Recent research has shown that a new face paradigm is superior to the conventional "flash only" approach that has dominated P300 brain-computer interfaces (BCIs) for over 20 years. However, these face paradigms did not study the repetition effects and the stability of evoked event related potentials (ERPs), which would decrease the performance of P...

In a brain-computer interface (BCI), users perform specific mental tasks to convey messages or commands through direct measures of brain activity. Typically, users must perform each mental task for two or more seconds before their brain activity is distinct enough for accurate classification. In P300 BCIs, this usually entails silently counting spe...

Affective states, moods and emotions, are an integral part of human nature: they shape our thoughts, govern the behavior of the individual, and influence our interpersonal relationships. The last decades have seen a growing interest in the automatic detection of such states from voice, facial expression, and physiological signals, primarily with th...

Over the last several years, brain-computer interface (BCI) research has grown well beyond initial efforts to provide basic communication for people with severe disabilities that prevent them from communicating otherwise. Since BCIs rely on direct measures of brain activity, users do not have to move in any way to convey information. During the ear...

Brain–computer interfaces have been improved dramatically over recent years and many new applications have been developed. This chapter describes some of the most important and interesting systems and concepts that are already available on the market or that will come to market soon: spelling, gaming, painting, avatar control, stroke rehabilitation...

The Fifth International Brain-Computer Interface (BCI) Meeting met on 3–7 June 2013 at the Asilomar Conference Grounds, Pacific Grove, California, USA. The conference included 19 workshops covering topics in brain-computer interface and brain-machine interface research. Topics included translation of BCIs into clinical use, standardization and cert...

Brain-computer interfaces (BCIs) usually rely on one of three types of signals: P300, SSVEP, or ERD. Recent work has shown that ‘hybrid’ BCIs that combine different types of signals may be superior to their simpler counterparts. This paper introduces a new type of hybrid P300/SSVEP BCI with a four-choice system. We compared this new approach to con...

Each of the preceding chapters reviewed one of the projects nominated for a 2012 BCI Award. Many of these projects have already led to new follow-up work, and the nominees are generally still very active in the research community. This concluding chapter presents the 2012 BCI Award winner, then explores some trends that have become apparent through...

Brain-Computer Interfaces (BCIs) are devices that translate a user’s brain activity into messages or commands (Wolpaw et al., 2002; Pfurtscheller et al., 2010; Wolpaw and Wolpaw, 2012). BCIs have four components: a signal acquisition system that records the user’s brain activity; a signal processing module that finds meaningful patterns within this...

Brain-Computer Interface (BCI) research and (future) applications raise important ethical issues that need to be addressed to promote societal acceptance and adequate policies. Here we report on a survey we conducted among 145 BCI researchers at the 4th International BCI conference, which took place in May–June 2010 in Asilomar, California. We asse...

Following the first and second workshop on affective brain-computer interfaces, held in conjunction with ACII in Amsterdam (2009) and Memphis (2011), the third workshop explores the advantages and limitations of using neurophysiological signals for the automatic recognition of affective and cognitive states, and the different ways to use this infor...

Objective. Today, the brain–computer interface (BCI) community lacks a standard method to evaluate an online BCI's performance. Even the most commonly used metric, the information transfer rate (ITR), is often reported differently, even incorrectly, in many papers, which is not conducive to BCI research. This paper aims to point out many of the exi...

Brain–Computer Interface (BCI) research has developed in the last decade so that BCIs are ready to be used with users outside the research labs. Although a wide range of assistive devices (ADs) exist, the additional usage of a BCI could improve the overall performance or applicability of such a combined system and is called hybrid BCI (hBCI). In th...

One of the most common types of brain-computer interfaces (BCIs) is called a P300 BCI, since it relies on the P300 and other event-related potentials (ERPs). In the canonical P300 BCI approach, items on a monitor flash briefly to elicit the necessary ERPs. Very recent work has shown that this approach may yield lower performance than alternate para...

Brain-computer interfaces (BCI) are communication systems that allow people to send messages or commands without movement. BCIs rely on different types of signals in the electroencephalogram (EEG), typically P300s, steady-state visually evoked potentials (SSVEP), or event-related desynchronization. Early BCI systems were often evaluated with a sele...

Brain-computer interface (BCI) systems translate brain activity into messages or commands. BCI studies that record from a dozen or more subjects typically report substantial variations in performance, as measured by accuracy. Usually, some subjects attain excellent (even perfect) accuracy, while at least one subject performs so poorly that effectiv...

Brain-computer interfaces (BCIs) are devices that enable people to communicate via thought alone. Brain signals can be directly translated into messages or commands. Until recently, these devices were used primarily to help people who could not move. However, BCIs are now becoming practical tools for a wide variety of people, in many different situ...

A brain-computer interface (BCI) enables communication without movement based on brain signals measured with electroencephalography (EEG). BCIs usually rely on one of three types of signals: the P300 and other components of the event-related potential (ERP), steady state visual evoked potential (SSVEP), or event related desynchronization (ERD). Alt...

Recent growth in brain-computer interface (BCI) research has increased pressure to report improved performance. However, different research groups report performance in different ways. Hence, it is essential that evaluation procedures are valid and reported in sufficient detail. In this chapter we give an overview of available performance measures...

We introduce a new type of BCI for continuous simultaneous two dimensional cursor control. Users tried to control the vertical position of a virtual ball via ERD activity associated with imagined movement while simultaneously controlling horizontal position with SSVEP activity resulting from visual attention. Ten subjects participated in one offlin...

Most brain–computer interfaces (BCIs) rely on one of three types of signals in the electroencephalogram (EEG): P300s, steady-state visually evoked potentials, and event-related desynchronization. EEG is typically recorded non-invasively with electrodes mounted on the human scalp using conductive electrode gel for optimal impedance and data quality....

Brain-computer interface (BCI) systems are not often used as input devices for modern games, due largely to their low bandwidth. However, BCIs can become a useful input modality when adapting the dynamics of the brain-game interaction, as well as combining them with devices based on other physiological signal to make BCIs more powerful and flexible...

Brain–computer interfaces (BCIs) are finally moving out of the laboratory and beginning to gain acceptance in real-world situations. As BCIs gain attention with broader groups of users, including persons with different disabilities and healthy users, numerous practical questions gain importance. What are the most practical ways to detect and analyz...

The performance of non-invasive electroencephalogram-based (EEG) brain–computer interfaces (BCIs) has improved significantly in recent years. However, remaining challenges include the non-stationarity and the low signal-to-noise ratio of the EEG, which limit the bandwidth and hence the available applications. Optimization of both individual compone...

brain-computer interface (BCI) operating protocol has four key elements, each of which can be stated as a question: Who initiates BCI operation: the BCI or the user? Who parameterizes the feature extraction and translation process: the BCI or its external support? Does the BCI tell its application what to do or how to do it? Does the BCI try to rec...

This chapter describes steady-state visual evoked potentials (SSVEPs), slow cortical potentials (SCPs), and brain-computer interfaces (BCIs) based on these signals. SSVEPs are produced by repetitive stimuli (e.g., a flashing light or a pattern-reversing checkerboard) and are focused over occipital cortex. With a rhythmic stimulus, they typically di...

Brain-computer interfaces (BCIs) allow users to communicate via brain activity alone. Many BCIs rely on the P300 and other event-related potentials (ERPs) that are elicited when target stimuli flash. Although there have been considerable research exploring ways to improve P300 BCIs, surprisingly little work has focused on new ways to change visual...