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Google Scholar. Diaz-Piedra, C. Morales, J. Miguel Catena, A. Romero, S. Roca-Gonzalez, J.

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Give Feedback. Get Information. Fuentes 6 and Leandro L. The functional validity of the signal obtained with low-cost electroencephalography EEG devices is still under debate. We evaluated the EEG recording quality by comparing the similarity between the temporal data series, their spectra, their signal-to-noise ratio, the reliability of EEG measurements comparing the closed eyes periods , as well as their blink detection rate. We found substantial agreement between signals: whereas, qualitatively, the NeuroSky MindWave presented higher levels of noise and a biphasic shape of blinks, the similarity metric indicated that signals from both recording devices were significantly correlated.

While the NeuroSky MindWave was less reliable, both devices had a similar blink detection rate. Overall, the NeuroSky MindWave is noise-limited, but provides stable recordings even through long periods of time. Furthermore, its data would be of adequate quality compared to that of conventional wet electrode EEG devices, except for a potential calibration error and spectral differences at low frequencies.

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Introduction Electroencephalography EEG , since its invention in the early s [ 1 ], has been one of the most commonly used techniques for neurological and psychological assessments. Traditionally, EEG measurements have been performed with highly sensitive electronic devices in an attempt to maximize the signal-to-noise ratio, and using multiple electrodes 32, 64, , or more—usually reusable—embedded in a stretch-lycra electrode cap or pasted to the scalp. Furthermore, they involve extensive training and experience for experimental setup and data collection [ 2 , 3 ].

However, despite early interest to explore brain activity in more realistic contexts, for example to improve workplace safety [ 6 ] or to assess sleepiness during day and night work [ 7 ], EEG has only slowly gained traction in real-world settings [ 8 , 9 ], mainly due to the bulkiness and cost of the equipment. Nevertheless, in the past ten years new EEG devices [ 10 ] and processing algorithms [ 11 ] have appeared that overcome many of these barriers for a recent review on this topic, see [ 12 ].

Recent advances in dry electrodes technology have facilitated the recording of EEG in situations not previously possible [ 16 , 17 ]. However, the functional validity of the EEG signal acquired with low-cost neurotechnologies is still under debate [ 19 ], and the quality accuracy and reliability of the data acquired with most of these low-cost EEG devices have not been fully proved yet [ 20 ].

Furthermore, the current adoption trend for this device [ 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ] makes it imperative to help researchers and final users understand its validity. However, quantitative studies of its actual validity for sensitively measuring EEG signals are limited to a manufacturer-provided white paper [ 29 ], the assessments carried out by Johnstone and colleagues of the previous version of this device [ 30 , 31 ], and another two works that compared the MindWave with wireless wearable EEG devices [ 32 , 33 ]. Thus, results on the functional validity of the MindWave are not conclusive and the question of whether MindWave might be reliable enough to track overall EEG signal remains open e.

Under well-controlled experimental conditions, we compared EEG signals acquired from virtually the same scalp place Fp1 vs AF3 while participants performed laboratory tasks e. Furthermore, considering the growing interest for implementing tools to monitor cognitive performance in naturalistic environments [ 9 ], EEG signals were acquired also during a 1-hour long every-day activity i. The results presented here give an accurate representation of the strengths and limits of the MindWave recording device, and delineate the most appropriate scenarios for its use in scientific applications.

We used medical history of significant head injury or neurological disorder as exclusion criteria. Furthermore, to reduce the influence of other potential confounder variables e. No participants were excluded based on these criteria. These two elements are mounted on a light headset 90 g.

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The device samples data at Hz. The MindWave electrodes are made of stainless steel and all connections use shielded cables. Energy is supplied by a single 1.

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The manufacturer has rated the device for continuous 8-hour operation on a single battery. Nevertheless, we took the precaution of changing the batteries after every 2 hours of use [ 37 ]. The headset uses a wireless Bluetooth connection to send EEG raw data to a recorder platform. Both the MindWave and the SOMNOwatch devices are small, wearable devices, reasonably affordable, intended for applied clinical or research studies, and require a short setup time.

It has been used for research purposes as well e. Thus, it is robust to movements and noise, as well as artifacts from electrode movement that lead to changes in contact impedance, or even the generation of a triboelectric response on the wires. In this setup, it can record EEG, electrooculographic EOG , and electromiographic data, as well as the position of the body.

The device samples data at Hz applying a band pass filter 0. Ground was placed at Fp2. We recorded vertical and horizontal EOG from the outer canthus of the right eye and below the left eye using a bipolar configuration. The device collects internally the raw EEG data. Resting state EEG no-task condition. We used a resting-state EEG experimental paradigm to analyze brain activity in the absence of any specific task.

The first period with the eyes open or closed was randomly assigned to participants.

We used the blinks bouts as biological triggers. A researcher author C. Participants were asked to hold still with their hands resting on their legs and to direct their gaze toward infinity in the direction of a blank wall during the open eyes session. Driving simulator task. We used a minute driving session to analyze brain activity while participants were performing an ecological and dynamic task requiring controlled attention but not excessive mental effort [ 21 ].

We developed a two-lane rounded rectangle virtual circuit using the OpenDS 2. The absence of traffic or intersections minimizes motion artifacts due to head movements, especially in a head-unrestrained condition. To control the car, participants used a Logitech G27 steering wheel steering wheel with active dual-motor force feedback, gas and brake pedals; Logitech International S.

Six loudspeakers located around the driver, at about ground level, provided the simulated surround sound of the engine. Speedometer and tachometer gauges were shown in the bottom right of the screen. During the driving period, the projected image on the wall provided the only light inside the simulation laboratory. The experiment took place in a simulation laboratory for more details see [ 41 ] , located at the Mind, Brain, and Behavior Research Center Granada, Spain. First, the participant signed the informed consent form. We performed an initial screening to assess inclusion and exclusion criteria and to collect information about sociodemographic characteristics and driving experience.

Gold electrodes were filled with conductive paste and pasted with collodion. Due to the instability of the MindWave EEG headset, the dry electrode was placed and secured with surgical tape to facilitate the adherence with the forehead skin. To reduce the impedance between skin and electrodes, to the extent possible, we ensured that hairs were put away [ 42 ].

Once participants were fitted with the devices and seated in the car seat, they filled in the SSS scale and drove during five minutes to familiarize themselves with the simulator. After that, they started the resting state EEG. Finally, the minute driving simulation started. Both signals were filtered using an order 10 Chebychev type II filter, which provides a sharp transition between passband and stopband without causing rippling in the former, to remove spectral components outside the [0.

The recordings were aligned using an information-theoretic delay criterion [ 43 ]. We segmented the five periods including the two cycles of closed eyes and open eyes conditions a 6-minute cycle , as well as the driving period a minute session. Before analyzing the quality of the recording signal-to-noise ratio [SNR] analysis and spectral estimation, see below , a threshold technique was used to identify and remove high-amplitude artifacts e. The ms previous and the ms following each crossing of the positive amplitude threshold were removed from the analysis enough to reject the full blink waveform.

The obtained thresholds were validated by visual inspection of the open eyes periods.