The BIK-TA BioInstrumentation Physiology Teaching Kit includes all of the necessary hardware and components, LabScribe software and expertly written courseware to teach 60 experiments and more than 175 exercises in biosensing cardiovascular, respiratory, and neuromuscular physiology. The BIK-TA kit also allows the student to signal condition, biosignals acquired from biosensors.
The kit includes:
Optional Add-On Sets:
LabScribe3 is a powerful recording and analysis software package developed by iWorx. LabScribe3 has an intuitive, user-friendly interface for setting up acquisition screens, calibrating signals and analyzing data.
iWorx API: Custom applications can be developed for iWorx Hardware using our API. Matlab and LabView can be used to record data from iWorx recorders.
Students can create experiments and study design of add-on circuits using the breadboard. Signal from any input channel of the IX-TA-220 can be routed to the stimulator and sent to the breadboard using the C-BNC-BB cable. Students have the ability to design a custom circuit to condition the signal. The conditioned signal can then be sent back to the IX-TA-220 using the C-DIN-BB cable, allowing the student to compare the raw biosignal and the conditioned signal in LabScribe.
The Stimulator in the IX-TA-220 can be setup to output waveforms, such as a square wave, sine wave ( upto 1kHz), pulses, steps, trains as well as custom waveforms. Create a custom waveform in Excel and paste it into the custom waveform generator, to output a custom waveform. Stimulator feedback channel allows the user to see the stimulator output. Stimulator toolbar allows changes to the protocol during recording.
8 digital inputs and 8 digital output are available. The Digital output can be preset to output a pattern, just like the stimulator.
These Lab Exercises can be performed with the BIK-TA kit. These experiments support courses in Biomedical, Bioengineering, Bioinstrumentation, Signal Conditioning, Physiological systems, etc…
|BI-01: ECG Signal Conditioning|
|BI-02: EMG Signal Conditioning|
|BI-03: ECG Noise|
|HC-01: BP, Peripheral Circulation and Body Position|
|HC-02: BP, Peripheral Circulation and Imposed Conditions|
|HC-03: Pulse Wave Velocity|
|HC-04: Pulse Contour Analysis|
|HC-05: Body Position, Exercise and Cardiac Output|
|HE-01: Metabolism and Thermal Response to Exercise|
|HE-02: Recovery from Exercise|
|HH-01: Electrocardiogram (ECG) and Peripheral Circulation|
|HH-02: ECG and Heart Sounds with Stethoscope|
|HH-03: Exercise, the ECG and Peripheral Circulation|
|HH-04: Six Lead ECG|
|HH-05A: The Diving Reflex|
|HH-06: Heart Rate Variability|
|HH-07: ECG using Six Chest Leads|
|HH-12: Pulse and Heart Rate Variability (HRV)|
|HM-01: Grip Strength and the Electromyogram|
|HM-02: Electromyogram Activity in Antagonistic Muscles|
|HM-03: Oculomotor Muscle Activity|
|HM-04: Stimulus Response, Work, Summation and Tetanus in Human Muscles|
|HM-08: Electromyogram (EMG) Activity while Arm Wrestling|
|HM-09: Kinesiology and Electromyogram (EMG) Activity in Targeted Muscles|
|HM-10: The Electrogastrogram (EGG) and Growling Stomach|
|HN-01: Auditory and Visual Reflexes|
|HN-02B: Stretch Receptors and Reflexes with Plethysmograph|
|HN-03: Human Nerve Conduction Velocity|
|HN-06: Hoffman Reflex using the Soleus Muscle|
|HN-07: Median Nerve Conduction Velocity|
|HN-08: Game Show Physiology|
|HP-01: The Electroencephalogram (EEG)|
|HP-05A: Heart Rate and Blood Pressure|
|HP-05C: Vigilance and Reaction Time|
|HP-06: Cynicism-Hostility and the Hot Reactor|
|HP-09: Facial Expression Electromyograms (EMG) and Emotion|
|HP-10: Visual Evoked Potentials (VEP)|
|HP-11: Multisensory Reaction Times|
|HP-13: The Gaze Cue Paradigm|
|HS-01: Breathing Parameters at Rest and After Exercise|
|HS-02: Breathing and Gravity|
|HS-03: Factors that Affect Breathing Patterns|
|HS-04: Lung Volumes and Heart Rate|
|HS-08: Restrictive and Obstructive Airway Diseases|
|TT-01: Tutorial with ECG|
|TT-02: Tutorial with pulse|
Martinovic, Ivan, and Vesna Mandic. “Biomedical Signals Reconstruction Under the Compressive Sensing Approach.” arXiv preprint arXiv:1802.00337 (2018).