A noninvasive, fast and inexpensive tool for the detection of eye open/closed state in primates
Introduction
Vision is the main sense by which primates (both human and nonhuman) perceive the world. Unlike other senses, visual input can be completely blocked at the level of the sensory organ by the eyelid. Therefore, understanding any neuronal activity involving the visual system requires an accurate recording of the state of the eyelids, i.e., whether they are open or closed. Furthermore, detecting the state of the eyelid is crucial for monitoring motor output during eyeblink conditioning (Marquis and Hilgard, 1937). Finally, detection of eyeblink enables the study of the natural frequency of blinking, which is altered in different pathological states such as schizophrenia or Parkinson's disease (Ponder and Kennedy, 1927, Stevens, 1978, Karson, 1983).
Several methods have been suggested for detection of the eye state of primates. One useful technique is electromyography (EMG) of the orbicularis oculi, the main muscle that is involved in blinking movement, and detecting its activation during eye closure (Silverstein et al., 1978, Blazquez et al., 2002). Another more direct method attaches the eyelid by a wire to a microtorque potentiometer that can measure its movements (Pennypacker et al., 1966). These methods are somewhat invasive, and it is unclear how these devices influence the natural movements of the eyelid.
Less invasive ways include connecting an electromagnetic search coil to the eyelid (Robinson, 1963, Porter et al., 1993). Here, a wire coil is secured to the upper eyelid of the animal, and placed in a weak magnetic field. This generates a current in the coil that is proportional to the angular velocity of the eyelid, thus enabling detection of changes in the state of the eye. Another noninvasive method uses an infrared light-emitting diode (LED) and a photo sensor (Thompson et al., 1994, Clark and Zola, 1998). However, this method requires placing the detector at a distance of 4–5 mm from the animal's eye, which may block significant parts of its field of view. These methods may be irritating to the primates, and therefore could influence their behavior.
The least invasive method that has been used by researchers is direct detection by a human observer. This is usually done offline, after videotaping the animal's behavior (e.g., Nevet et al., 2004). However this method is very cumbersome and time consuming, and therefore is not feasible for processing large amounts of data. Furthermore, human observers are prone to mistakes when asked to classify long video sequences and may be biased by their a priori expectations.
Several automatic visual analysis based methods have been suggested for eye state detection in other mammals. In humans there are several algorithms (Tian et al., 2000, Miyakawa et al., 2004, Benoit and Caplier, 2005, Tan and Zhang, 2006, Heishman and Duric, 2007), but they are rather complex, and do not take into account some of the differences between human and non-human primates (e.g. the difference in the sclera's relative size). Moreover, these algorithms are primarily designed for non-scientific goals such as driver fatigue detection, and are intended to achieve impressive stability under unsupervised circumstances. On the other hand, they do not take advantage of the typical primate physiological recording setting, and fall below the performance level of human observers. A system that was suggested for use in rabbits (Bracha et al., 2003) has the disadvantage of attaching markers on the upper and lower eyelid of the animal and therefore is less suitable for daily repeating recording sessions that are typical of physiological studies of awake behaving primates.
In this manuscript we suggest a simple, noninvasive and inexpensive video-based method to detect the state of the eye of primates under head immobilization conditions. The system takes advantage of the typical setting of primate physiological experiments, and operates on the basis of minimal changes in the position of the eyes during a recording session. The video camera can be positioned at a distance from the monkey (depending on its zoom properties) and therefore does not obscure the visual field and does not modify natural blinking behavior. The method is also highly accurate, with a performance level equivalent to that of a human observer (a mean normalized error of 0.15%). Furthermore, since this method works with infrared videotaping, the eye state can be detected in a dark environment.
Section snippets
Materials and methods
The tool we describe in this paper includes standard hardware and simple custom-made software. We present the hardware we used in the experiments, and the way we chose to implement the algorithm, although any equivalent hardware and software implementation can be employed.
Algorithm performance and stability
The eye state detection algorithm is based on two thresholds. As described above, the first is the brightness threshold, which determines how dark (on a scale of 0–255) a pixel needs to be so as to be considered black and is set by the user during the training stage. The second is the eye state threshold, which determines the state of the eye according to the number of black pixels, and is calculated automatically (based on the user's classification during the training stage). Note that these
Discussion
This manuscript describes a fast, simple, inexpensive and noninvasive tool for eye state detection during electrophysiological studies of primates. It is adapted to perform optimally in the typical setting of primate physiological studies; e.g., head fixation (Lemon, 1984). This type of tool is valuable for many primate studies, and can be easily adapted to most setups, since the only hardware it requires is a digital surveillance camera. The use of such camera, which is sensitive to the
Acknowledgement
This work was partly supported by a Hebrew University Netherlands Association grant entitled “Fighting against Parkinson”, and the Harry and Sylvia Hoffman leadership and responsibility program. We would like to thank E. Singer for language editing.
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These authors contributed equally.