Validation of Electroencephalographic Recordings Obtained with a Consumer-Grade, Single Dry Electrode, Low-Cost Device: A Comparative Study

Figure 1. Experiment structure. (a) EEG recording configuration. Red elements and arrows indicate the electrodes used by the SOMNOwatch device. Blue elements and arrows indicate electrodes used by the MindWave device. (b) UML (Unified Modeling Language) activity diagram for the implementation and data acquisition of the experiment. The session started with the placement of the electrodes. After checking the signal quality, EEG data collection started. The experiment started with the resting state task (~15 min) structured as two cycles of task 1 (closed eyes, 3 min each) and task 2 (open eyes, 3 min each). The experiment started with either task 1 or task 2, as the order was random for each participant. Afterwards, the driving task (task 3) started (a 60-minute driving session without breaks). MindWave data was visualized in real time (RTD visualization) for all tasks. Once the three tasks finished, the session ended.

Figure 2. Data processing. UML (Unified Modeling Language) activity diagram for the experimental data processing. The process started by checking the device type. For the MindWave, we read the data file and downsampled it to 256 Hz; for the SOMNOwatch, we just read the data file (already at 256 Hz). Next, we applied an Order 10 Chebychev type II filter, followed by a signal alignment. We segmented the data into the three tasks (closed eyes, open eyes, and driving tasks). For each task, we detected blink artifacts, calculated the similarity measure, and removed high-amplitude artifacts, to finally compute the signal-to-noise ratio (SNR) and to perform the spectral estimation. Note that rectangles indicate processes, diamonds indicate decisions, and parallelograms indicate output data.

Figure 3. Differences between recording sites (Fp1 and AF3) and reference sites (mastoid and lobe) when recorded with the SOMNOwatch. (a) EEG recording configuration. Red arrows indicate the four electrodes’ placements of the SOMNOwatch used to compare the data. (b) Linear regression model for Fp1 and AF3 when recorded with the SOMNOwatch for five participants. The cloud of points shows the data for each subject first centered (by subtracting the average) and divided by its standard deviation, while the solid line represents the result of a linear regression of the form AF3 = b + g × Fp1. The numerical results for the regression and the correspondent determination coefficient are shown in the graph inset. (c) Linear regression model for Mastoid and Lobe references when recorded with the SOMNOwatch for five participants. In this case, the solid line represents the result of a linear regression of the form Lobe = b + g × Mastoid.

Figure 4. Comparison of temporal data series. (a) Left panel shows example traces of a simultaneous recording in one participant. The different noise levels and different shape of blinks are easy to observe. (b) The right panel shows the similarity measures (open circles) between the recordings for each participant and each of the three separate tasks (closed eyes, open eyes, driving task), as compared to a baseline value (dotted lines at the bottom). The values for each subject are displaced on the horizontal axis for representation purposes.

Figure 5. Spectral comparison between recording devices. (a) Spectrograms of the simultaneous recordings, in a single participant, with the two acquisition devices. The different tasks (open eyes, closed eyes, and the driving task) are delineated in the temporal axis. While the recordings are qualitatively similar, a higher level of noise can be appreciated in the MindWave data. (b) Power spectral densities obtained from the closed eyes (left), open eyes (center), and driving (right) tasks. Thin lines show individual participants, thick lines the average result. The devices differed in their response at lower frequencies, as evidenced by the MindWave peak around 3Hz.

Figure 6. Waveform and rate of detected blinks. Average waveforms and blink detection rate for each individual participant (N = 21, thin lines) and the population mean (thick lines). (a) Average waveform, with the timepoint of crossing the amplitude threshold (see Methods section) aligned to zero. Amplitudes of individual artifacts are normalized to a maximum value of 1. The different shape of blinks is apparent. (b) Blink detection rate obtained from the closed eyes (left), open eyes (center), and driving (right) tasks. Thin lines show individual participants and thick lines are the average result.

Figure 7. Signal-to-noise ratio (SNR) estimation. (a) Additive white noise model employed for the estimation of the SNR. The physiological signal x is filtered by the impulse response, resulting in filtered signal y, of both recording devices, at which point white noise (w) is added, resulting in the recorded signals. The SNR is defined as the ratio between the power of the filtered signal and the power of noise. (b) Results for each participant (thin lines) and average (thick line) the closed eyes (left), open eyes (center), and driving (right) tasks. SNR for the SOMNOwatch is on average 2 dB above that of the MindWave.

Figure 8. Normalized alpha waves as simultaneously captured by both devices during periods of closed eyes and open eyes. Alpha waves were separated from the rest of the signal using a 10th-order Chebychev filter. The alpha amplitude is clearly increased in closed eyes periods.