Is the low-cost EyeTribe eye tracker any good for research?

Dalmaijer, Edwin S.

doi:10.7287/peerj.preprints.585v1

Cited by 88 publications

(98 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In addition, miniaturized mobile EEG systems (De Vos et al, 2014; Stopczynski et al, 2014) and NIRS systems are presently being developed (Piper et al, 2014; Von Lühmann et al, 2015). New affordable eye trackers (Dalmaijer, 2014) and the increasing commercial interest in wearable physiological sensors, such as heart-rate sensors in smart watches or a glucose sensor in a contact lens (“Google Contact Lens”), likewise promise to be beneficial for further development.…”

Section: Discussionmentioning

confidence: 99%

The Berlin Brain-Computer Interface: Progress Beyond Communication and Control

et al. 2016

View full text Add to dashboard Cite

The combined effect of fundamental results about neurocognitive processes and advancements in decoding mental states from ongoing brain signals has brought forth a whole range of potential neurotechnological applications. In this article, we review our developments in this area and put them into perspective. These examples cover a wide range of maturity levels with respect to their applicability. While we assume we are still a long way away from integrating Brain-Computer Interface (BCI) technology in general interaction with computers, or from implementing neurotechnological measures in safety-critical workplaces, results have already now been obtained involving a BCI as research tool. In this article, we discuss the reasons why, in some of the prospective application domains, considerable effort is still required to make the systems ready to deal with the full complexity of the real world.

show abstract

Section: Discussionmentioning

confidence: 99%

The Berlin Brain-Computer Interface: Progress Beyond Communication and Control

et al. 2016

View full text Add to dashboard Cite

show abstract

“…Until recently, the physiological measurement devices were bulky and expensive and the set-up was inconvenient and time consuming. Yet, the situation is improving at present due to technological innovations such as miniaturized, gel-free, and in-ear as well as around-ear electrodes [56–61] and a considerable drop in the price of eye trackers [62]. Further impulses can be expected from large tech companies that launch wearable physiological sensors as parts of their products, like heart-rate sensors in smart watches, or that are working on miniaturized glucose sensors in contact lenses.…”

Section: Discussionmentioning

confidence: 99%

Is Neural Activity Detected by ERP-Based Brain-Computer Interfaces Task Specific?

2016

View full text Add to dashboard Cite

ObjectiveBrain-computer interfaces (BCIs) that are based on event-related potentials (ERPs) can estimate to which stimulus a user pays particular attention. In typical BCIs, the user silently counts the selected stimulus (which is repeatedly presented among other stimuli) in order to focus the attention. The stimulus of interest is then inferred from the electroencephalogram (EEG). Detecting attention allocation implicitly could be also beneficial for human-computer interaction (HCI), because it would allow software to adapt to the user’s interest. However, a counting task would be inappropriate for the envisaged implicit application in HCI. Therefore, the question was addressed if the detectable neural activity is specific for silent counting, or if it can be evoked also by other tasks that direct the attention to certain stimuli.ApproachThirteen people performed a silent counting, an arithmetic and a memory task. The tasks required the subjects to pay particular attention to target stimuli of a random color. The stimulus presentation was the same in all three tasks, which allowed a direct comparison of the experimental conditions.ResultsClassifiers that were trained to detect the targets in one task, according to patterns present in the EEG signal, could detect targets in all other tasks (irrespective of some task-related differences in the EEG).SignificanceThe neural activity detected by the classifiers is not strictly task specific but can be generalized over tasks and is presumably a result of the attention allocation or of the augmented workload. The results may hold promise for the transfer of classification algorithms from BCI research to implicit relevance detection in HCI.

show abstract

“…Only a pupillometer, or an eye tracker that records pupil size, is required. For most experiments, we have used a research-grade eye tracker; but we have also successfully used an EyeTribe (The Eye Tribe Aps, Copenhagen, Denmark), a low-cost eye tracker that provides highquality pupil-size measurements (Dalmaijer, 2014) . Our method does not require eye-tracker calibration, nor training of the selection algorithm.…”

Section: Discussionmentioning

confidence: 99%

The mind-writing pupil: A human-computer interface based on decoding of covert attention through pupillometry

Mathôt

Melmi

Linden

et al. 2015

Preprint

View full text Add to dashboard Cite

We present a new human-computer interface that is based on decoding of attention through pupillometry. Our method builds on the recent finding that covert visual attention affects the pupillary light response: Your pupil constricts when you covertly (without looking at it) attend to a bright, compared to a dark, stimulus. In our method, participants covertly attend to one of several letters with oscillating brightness. Pupil size reflects the brightness of the selected letter, which allows us-with high accuracy and in real time-to determine which letter the participant intends to select. The performance of our method is comparable to the best covert-attention brain-computer interfaces to date, and has several advantages: no movement other than pupil-size change is required; no physical contact is required (i.e. no electrodes); it is easy to use; and it is reliable. Potential applications include: communication with totally locked-in patients, training of sustained attention, and ultra-secure password input. : human-computer interface, brain-computer interface, pupillometry, covert visual attention Manuscript in preparation [v1.1.4; Wed Sep 9 13:22:41 2015; There are many types of BCIs (reviewed in Donoghue, 2008;Nicolas-Alonso & Gomez-Gil, 2012) , which differ in the neural signal that they use (e.g., neural spikes or electroencephalography [EEG]), the way that neural activity is processed (e.g., through a classifier or by measuring overall activity in specific brain areas), and the actions that they perform (e.g. controlling a robotic limb, or writing text). Here we present a new method, which uses pupil size, rather than brain activity, as the controlling signal. Our method is related to two existing methods: the P300 speller (Farwell & Donchin, 1988), which is functionally similar to our method but relies on a different controlling signal; and a recent pupillometry-based method (Stoll et al., 2013) , which is functionally different from our method but relies on the same controlling signal. P300 spellers are among the most successful BCIs (reviewed in Fazel-Rezai et al., 2012) . They exploit the fact that rare visual stimuli elicit positive deflections in the EEG signal about 300 ms after their appearance. This event-related-potential (ERP) component is called the P300, and is largest for stimuli that are overtly (while looking at them) or covertly (without looking at them) attended (e.g., Treder & Blankertz, 2010). In a classic P300 speller, the participant sees a grid of letters. One letter, or sometimes one full column or row of letters, is highlighted at a time. A P300 is elicited each time that a letter is highlighted. The participant selects a letter by attending to it, usually by looking at it directly (i.e. overt attention), which leads to an increased P300 when that letter is highlighted. In its simplest form, the letter that, when highlighted, elicits the highest P300 is selected; however, most P300 spellers now use sophisticated classification techniques, and pool information from mult...

show abstract

Is the low-cost EyeTribe eye tracker any good for research?

Cited by 88 publications

References 8 publications

The Berlin Brain-Computer Interface: Progress Beyond Communication and Control

The Berlin Brain-Computer Interface: Progress Beyond Communication and Control

Is Neural Activity Detected by ERP-Based Brain-Computer Interfaces Task Specific?

The mind-writing pupil: A human-computer interface based on decoding of covert attention through pupillometry

Contact Info

Product

Resources

About