title: Multiple frequency audio signal communication as a mechanism for neurophysiology and video data synchronization.
abstract: BACKGROUND
  Current methods for aligning neurophysiology and video data are either prepackaged, requiring the additional purchase of a software suite, or use a blinking LED with a stationary pulse-width and frequency. These methods lack significant user interface for adaptation, are expensive, or risk a misalignment of the two data streams.
  
  
  METHODS
  A cost-effective means to obtain high-precision alignment of behavioral and neurophysiological data is obtained by generating an audio-pulse embedded with two domains of information, a low-frequency binary-counting signal and a high, randomly changing frequency. This enabled the derivation of temporal information while maintaining enough entropy in the system for algorithmic alignment.
  
  
  RESULTS
  The sample to frame index constructed using the audio input correlation method described in this paper enables video and data acquisition to be aligned at a sub-frame level of precision.
  Traditionally, a synchrony pulse is recorded on-screen via a flashing diode. The higher sampling rate of the audio input of the camcorder enables the timing of an event to be detected with greater precision.
  
  
  CONCLUSIONS
  While on-line analysis and synchronization using specialized equipment may be the ideal situation in some cases, the method presented in the current paper presents a viable, low cost alternative, and gives the flexibility to interface with custom off-line analysis tools. Moreover, the ease of constructing and implements this set-up presented in the current paper makes it applicable to a wide variety of applications that require video recording.
authors.affiliation: McKnight Brain Institute, Department of Neuroscience University of Florida, Gainesville, FL 32610, United States.
authors.forename: Nicholas C
authors.initials: NC
authors.lastname: Topper
authors.affiliation: McKnight Brain Institute, Department of Neuroscience University of Florida, Gainesville, FL 32610, United States.
authors.forename: Sara N
authors.initials: SN
authors.lastname: Burke
authors.affiliation: Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ 85724, United States; Arizona Research Laboratories Division of Neural Systems Memory and Aging, University of Arizona, Tucson, AZ 85724, United States; McKnight Brain Institute, Department of Neuroscience University of Florida, Gainesville, FL 32610, United States. Electronic address: drewmaurer@ufl.edu.
authors.forename: Andrew Porter
authors.initials: AP
authors.lastname: Maurer
mesh_terms: D000161:Acoustic Stimulation / Q000295:instrumentation / Q000379:methods*; D000465:Algorithms; D000818:Animals; D001921:Brain / Q000502:physiology; D004867:Equipment Design; D009043:Motor Activity / Q000502:physiology; D009482:Neurophysiology / Q000295:instrumentation / Q000379:methods*; D012984:Software; D013696:Temperature; D013997:Time Factors; D014741:Video Recording / Q000295:instrumentation / Q000379:methods*
keywords: Alignment; Behavior; Brain; In vivo recording; Microcontroller