The mTRF Toolbox is a MATLAB repository that permits the fast computation of the linear stimulus-response mapping of any sensory system in the forward or backward direction. It is suitable for analysing multi-channel EEG, MEG, ECoG and EMG data. The forward encoding model, or temporal response function (TRF) as it is commonly known, can be used to investigate information processing in neuronal populations using conventional time- frequency and source analysis techniques. In addition, TRFs can be used to predict the spectrotemporal dynamics of future neural responses to unseen stimulus sequences. Stimulus reconstruction can also be performed using backward decoding models that project the multi-channel population responses back to the dynamics of the causal stimulus signal. The mTRF Toolbox facilitates the use of extended continuous stimuli in electrophysiological studies compared to conventional time-locked averaging approaches which require the use of discrete, isolated stimulus events. This allows researchers to investigate of how neural systems process dynamic environmental signals such as speech, music and motion.
- Ensure that the stimulus and response data have the same sample rate and number of samples.
- Downsample the data when conducting large-scale multivariate analyses to reduce running time, e.g., 128 Hz or 64 Hz.
- Normalise or standardise the data beforehand. We recommend normalising by the standard deviation. This will stabalise regularization across trials/subjects/groups and facilitate a smaller parameter search.
- Enter the start and finish time lags in milliseconds. Enter positive lags for post-stimulus mapping and negative lags for pre-stimulus mapping. This is the same for both forward and backward mapping - the code will automatically reverse the lags for backward mapping.
- When using MTRFPREDICT, always enter the model in its original 3-dimensional form, i.e., do not remove any singleton dimensions.
- When using MTRFCROSSVAL, the trials do not have to be the same length, but using trials of the same length will optimise performance.
- When using MTRFMULTICROSSVAL, the trials in each of the three sensory conditions should correspond to the stimuli in STIM.
mTRF Toolbox is also available for download at: SourceForge: http://sourceforge.net/projects/aespa
mTRF Toolbox support documentation is available at: http://dx.doi.org/10.3389/fnhum.2016.00604
For any questions and comments, please email: mickcrosse@gmail.com (Mick Crosse) or edmundlalor@gmail.com (Ed Lalor)