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Real-World Neuroimaging Technologies

Figure 1

Figure 1
Prototype Real-World Neuroimaging System. Four components are illustrated, an Android device for data collection and integration, a laboratory grade, dry electrode, wirelesss, high resolution EEG system, a Bioharness 3, and an Affectiva Qsensor. Android screenshots depict some examples of activity logging components, such as a start/stop logger (left), two self-assessment surveys (middle), and a visuomotor continuous tracking task (right).

Figure 2

Figure 2
State-of-the Art Laboratory Grade EEG System made “Mobile.” Participant wears a high-density wet-electrode cap which is wired to a laptop and amplifiers in a backpack. This system takes approximately 1 hour to set-up, and can become uncomfortable in minutes.

Figure 3

Figure 3
An early prototype system for BCITs. The NCTU-developed MINDO-4 system for potential BCITs that detect and mitigate fatigue-related performance decrements during driving. System uses soft pads (U.S. penny and pen shown for scale) that snap into a headband, and streams data via Bluetooth for easy system integration.

Figure 4

Figure 4
Data quality for dry EEG systems are comparable to that of conventional “wet” gel-based approaches for both brain and muscle activity (top panels). Further, dry electrodes have much more stable impedance over time as compared to gel electrodes, which tend to dry out, increasing the contact impedance over time (bottom, adapted from [48]).

Figure 5

Figure 5
Stretchable, pliable conductors for biosensing. Example prototype 9-sensor strip of stretchable, pliable materials usable for EEG data collection can stretch beyond 33%. This technology allows it to easily conform for differing head sizes (left), while also providing comfort and safety of the user when built into head protection devices.

Figure 6

Figure 6
Performance of AR-based artifact identification methods. Discrimination accuracy is extremely high for subject-specific models of 7 different type of movement artifacts (A). Independent features are easily visualized when separated using Principle Component Analysis (first 3 components shown, B).

Figure 7

Figure 7
Sample prototype “phantom head” devices developed under the SBIR program. Models provided by CFDRC (left), POC (center), and Creare (right) are designed to be conductively analogous to the human head, with embedded sources to create realistic electrical dipoles detectable at the surface.

Figure 8

Figure 8
RSVP-based Adaptive Virtual Environment with Neural-processing (RAVEN) system. RAVEN system main operator screen and RSVP window. The primary task is identification of dismounts while the vehicle is navigating a simulated environment. Secondary tasks include identification of potential IED locations, monitoring and responding to communications (audio and text). The BCI component is engaged during RSVP search (Mode 3) and top scoring images are presented on the main operator screen.

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