Tuning Up the Old Brain with New Tricks: Attention Training via Neurofeedback

Jiang, Yang; Abiri, Reza; Zhao, Xiaopeng

doi:10.3389/fnagi.2017.00052

Cited by 51 publications

(41 citation statements)

References 95 publications

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“…In a pilot study consisting of six participants, the EEG data was collected while the participant attended to only one subcategory of images during the blocks of streaming of composite images. The developed platform may be employed in diagnosis of attention deficit in early stage of dementia or Mild Cognitive Impairment (MCI) in elderly people [5]. The platform may also be used as a method to assess the attentional levels in children to diagnose ADHD.…”

Section: Discussion and Future Workmentioning

confidence: 99%

“…Sustained attention refers to a cognitive capability to maintain focus during a task [2]. Deficits in attention are commonly seen in various brain disorders such as Alzheimer's disease (AD) and related dementia, Traumatic Brain Injuries (TBI), and Posttraumatic Stress Disorder (PTSD) [3][4][5]. Improvement in attentional states may assist these populations in boosting cognitive and perceptual functions such as working memory [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

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Decoding Attentional State to Faces and Scenes Using EEG Brainwaves

et al. 2019

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Attention is the ability to facilitate processing perceptually salient information while blocking the irrelevant information to an ongoing task. For example, visual attention is a complex phenomenon of searching for a target while filtering out competing stimuli. In the present study, we developed a new Brain-Computer Interface (BCI) platform to decode brainwave patterns during sustained attention in a participant. Scalp electroencephalography (EEG) signals using a wireless headset were collected in real time during a visual attention task. In our experimental protocol, we primed participants to discriminate a sequence of composite images. Each image was a fair superimposition of a scene and a face image. The participants were asked to respond to the intended subcategory (e.g., indoor scenes) while withholding their responses for the irrelevant subcategories (e.g., outdoor scenes). We developed an individualized model using machine learning techniques to decode attentional state of the participant based on their brainwaves. Our model revealed the instantaneous attention towards face and scene categories. We conducted the experiment with six volunteer participants. The average decoding accuracy of our model was about 77%, which was comparable with a former study using functional magnetic resonance imaging (fMRI). The present work was an attempt to reveal momentary level of sustained attention using EEG signals. The platform may have potential applications in visual attention evaluation and closed-loop brainwave regulation in future.

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Section: Discussion and Future Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Decoding Attentional State to Faces and Scenes Using EEG Brainwaves

et al. 2019

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“…The Enhancement category is defined by practical reasons, identity or self-esteem factors, but also the motivation to change how life is experienced. Practical reasons are defined by goals that range from the general enhancement of human abilities and intelligence T A B L E 3 Identified neurotechnology applications in the category Enhancement Invasive Noninvasive -Seismic Sense; allowing users to feel earth (CNN, 2018;Garcia, 2015;Quito, 2016) -Improved vocabulary learning and maintenance by tDCS (Meinzer et al, 2014) -Magnet in finger for magnetic feeling and magnetic capabilities (Robertson, 2017) -Film narrative control based on brain waves (Pike et al, 2016) -Control doors, lights, and other household devices (Warwick, 2019a) -Increase attention and working memory by neurofeedback (Jiang, Abiri, & Zhao, 2017) or by tDCS (Fregni et al, 2005) -Feeling magnetic north (pierced) (Thaddeus-Johns, 2017)…”

Section: Motivationsmentioning

confidence: 99%

Human enhancement through the lens of experimental and speculative neurotechnologies

Teunisse

Youssef

Schmidt

2019

Human Behav and Emerg Tech

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Human enhancement deals with improving on and overcoming limitations of the human body and mind. Pharmaceutical compounds that alter consciousness and cognitive performance have been used and discussed for a long time. The prospect of neurotechnological applications such as brain‐steered devices or using invasive and noninvasive electromagnetic stimulations of the human brain, however, has received less attention—especially outside of therapeutic practices—and remains relatively unexplored. Reflection and debates about neurotechnology for human enhancement are limited and remain predominantly with neurotech engineers, science‐fiction enthusiasts and a small circle of academics in the field of neuroethics. It is well known, and described as the Collingridge dilemma, that at an early stage of development, changes can easily be enacted, but the need for changes can hardly be foreseen. Once the technology is entrenched, opportunities and risks start to materialize, and the need to adapt and change is clearly visible. However, carrying out these changes at such a late stage, in turn, becomes very difficult, tremendously expensive, and sometimes practically impossible. In this manuscript, we compile and categorize an overview of existing experimental and speculative applications of neurotechnologies, with the aim to find out, if these real or diegetic prototypes could be used to better understand the paths these applications are forging. In particular, we will investigate what kind of tools, motivations, and normative goals underpin experimental implementations by neurohackers, speculative designers and artists.

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“…Neurofeedback uses real-time modulation of brain activity to regulate or enhance brain function and behavioral performance (for reviews, see [1,2,3,4]). A closed-loop feedback approach refers to a continuous monitoring of brain activity which forms the basis for a signal that is sent back to the user of the system in real time (for reviews, see [5,4]).…”

Section: Introductionmentioning

confidence: 99%

“…Regarding the use of neurofeedback in healthy individuals, also denoted as the "optimal" or "peak performance" field [38], there is evidence in support of cognitive performance enhancement (see three part review [1,39,40]). Another field of research which holds great potential and is still relatively unexplored, is the use of neurofeedback for mitigating cognitive impairment in the aging population (for review, see [3]).…”

Section: Introductionmentioning

confidence: 99%

A framework for closed-loop neurofeedback for real-time EEG decoding

Tuckute

Kjaer

et al. 2019

Preprint

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Neurofeedback based on real-time brain imaging allows for targeted training of brain activity with demonstrated clinical applications. A rapid technical development of electroencephalography (EEG)-based systems and an increasing interest in cognitive training has lead to a call for accessible and adaptable software frameworks. Here, we present and outline the core components of a novel open-source neurofeedback framework based on scalp EEG signals for real-time neuroimaging, state classification and closed-loop feedback.The software framework includes real-time signal preprocessing, adaptive artifact rejection, and cognitive state classification from scalp EEG. The framework is implemented using exclusively Python source code to allow for diverse functionality, high modularity, and easy extendibility of software development depending on the experimenter's needs.As a proof of concept, we demonstrate the functionality of our new software framework by implementing an attention training paradigm using a consumer-grade, dry-electrode EEG system. Twenty-two participants were trained on a single neurofeedback session with behavioral pre-and post-training sessions within three consecutive days. We demonstrate a mean decoding error rate of 34.3% (chance=50%) of subjective attentional states. Hence, cognitive states were decoded in real-time by continuously updating classification models on recently recorded EEG data without the need for any EEG recordings prior to the neurofeedback session.The proposed software framework allows a wide range of users to actively engage in the development of novel neurofeedback tools to accelerate improvements in neurofeedback as a translational and therapeutic tool.

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Tuning Up the Old Brain with New Tricks: Attention Training via Neurofeedback

Cited by 51 publications

References 95 publications

Decoding Attentional State to Faces and Scenes Using EEG Brainwaves

Decoding Attentional State to Faces and Scenes Using EEG Brainwaves

Human enhancement through the lens of experimental and speculative neurotechnologies

A framework for closed-loop neurofeedback for real-time EEG decoding

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