High resolution, in vivo optical imaging of the mouse brain over time often requires anesthesia, which necessitates maintaining the animal's body temperature and level of anesthesia, as well as securing the head in an optimal, stable position. Controlling each parameter usually requires using multiple systems. Assembling multiple components into the small space on a standard microscope stage can be difficult and some commercially available parts simply do not fit. Furthermore, it is time-consuming to position an animal in the identical position over multiple imaging sessions for longitudinal studies. This is especially true when using an implanted gradient index (GRIN) lens for deep brain imaging. The multiphoton laser beam must be parallel with the shaft of the lens because even a slight tilt of the lens can degrade image quality. In response to these challenges, we have designed a compact, integrated in vivo imaging support system to overcome the problems created by using separate systems during optical imaging in mice. It is a single platform that provides (1) sturdy head fixation, (2) an integrated gas anesthesia mask, and (3) safe warm water heating. This THREE-IN-ONE (TRIO) Platform has a small footprint and a low profile that positions a mouse's head only 20 mm above the microscope stage. This height is about one half to one third the height of most commercially available immobilization devices. We have successfully employed this system, using isoflurane in over 40 imaging sessions with an average of 2 h per session with no leaks or other malfunctions. Due to its smaller size, the TRIO Platform can be used with a wider range of upright microscopes and stages. Most of the components were designed in SOLIDWORKS® and fabricated using a 3D printer. This additive manufacturing approach also readily permits size modifications for creating systems for other small animals.

Non-invasive electroencephalography (EEG) based brain-computer interface (BCI) is able to provide an alternative means of communicat ion with and control over external assistive devices. In general, EEG is insufficient to obtain detailed information about many degrees of freedom (DOF) for arm movements. The main objectives are to design a non-invasive BCI and create a signal decoding strategy that allows people with limited motor control to have more command over potential prosthetic devices. Eight healthy subjects were recruited to perform visual cues directed reaching tasks. Eye and motion artifacts were identified and removed to ensure that the subjects' visual fixation to the target locations would have litt le or no impact on the final result. We applied a Fisher Linear Discriminate (FLD) classifier to perform single-trial classification of the EEG to decode the intended arm movement in the left , right, and forward directions (before the onsets of actual movements). The mean EEG signal amplitude near the PPC region 271-310ms after visual stimu lation was found to be the dominant feature for best classification results. A signal scaling factor developed was found to imp rove the classification accuracy fro m 60.11% to 93.91% in the binary class (left versus right) scenario. This result demonstrated great promises for BCI neuroprosthetics applications, as motor intention decoding can be served as a prelude to the classification of imag ined motor movement to assist in motor disable rehabilitation, such as prosthetic limb or wheelchair control.

Noninvasive electroencephalography (EEG) brain computer interface (BCI) systems are used to investigate intended arm reaching tasks. The main goal of the work is to create a device with a control scheme that allows those with limited motor control to have more command over potential prosthetic devices. Four healthy subjects were recruited to perform various reaching tasks directed by visual cues. Independent component analysis (ICA) was used to identify artifacts. Active post parietal cortex (PPC) activation before arm movement was validated using EEGLAB. Single-trial binary classification strategies using support vector machine (SVM) with radial basis functions (RBF) kernels and Fisher linear discrimination (FLD) were evaluated using signal features from surface electrodes near the PPC regions. No significant improvement can be found by using a nonlinear SVM over a linear FLD classifier (63.65% to 63.41% accuracy). A significant improvement in classification accuracy was found when a normalization factor based on visual cue "signature" was introduced to the raw signal (90.43%) and the intrinsic mode functions (IMF) of the data (93.55%) using Ensemble Empirical Mode Decomposition (EEMD).

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