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Assessment of Audio-Tactile Sensory Substitution Training in Participants with Profound Deafness Using the Event-Related Potential Technique

Published: September 7, 2022 doi: 10.3791/64266


This protocol is designed to explore underlying learning-related electrophysiological changes in subjects with profound deafness after a short training period in audio-tactile sensory substitution by applying the event-related potential technique.


This paper examines the application of electroencephalogram-based methods to assess the effects of audio-tactile substitution training in young, profoundly deaf (PD) participants, with the aim of analyzing the neural mechanisms associated with vibrotactile complex sound discrimination. Electrical brain activity reflects dynamic neural changes, and the temporal precision of event-related potentials (ERPs) has proven to be key in studying time-locked processes while performing behavioral tasks that involve attention and working memory.

The current protocol was designed to study electrophysiological activity in PD subjects while they performed a continuous performance task (CPT) using complex-sound stimuli, consisting of five different animal sounds delivered through a portable stimulator system worn on the right index finger. As a repeated-measures design, electroencephalogram (EEG) recordings in standard conditions were performed before and after a brief training program (five 1 h sessions over 15 days), followed by offline artifact correction and epoch averaging, to obtain individual and grand-mean waveforms. Behavioral results show a significant improvement in discrimination and a more robust P3-like centroparietal positive waveform for the target stimuli after training. In this protocol, ERPs contribute to the further understanding of learning-related neural changes in PD subjects associated with audio-tactile discrimination of complex sounds.


Early profound deafness is a sensory deficit that strongly impacts oral language acquisition and the perception of environmental sounds that play an essential role in navigating everyday life for those with normal hearing. A preserved and functional auditory sensory pathway allows us to hear footsteps when someone is approaching out of visual range, react to oncoming traffic, ambulance sirens, and security alarms, and respond to our own name when someone needs our attention. Audition is, therefore, a vital sense for speech, communication, cognitive development, and timely interaction with the environment, including the perception of potential threats in one's surroundings. For decades, the viability of audio-tactile substitution as an alternative sound perception method with the potential to complement and facilitate language development in severely hearing-impaired individuals has been explored with limited results1,2,3. Sensory substitution aims to provide users with environmental information through a human sensory channel different from the one normally used; it has been demonstrated to be possible across different sensory systems4,5. Specifically, audio-tactile sensory substitution is achieved when skin mechanoreceptors can transduce the physical energy of soundwaves that compose auditory information into neuronal excitation patterns that can be perceived and integrated with the somatosensory pathways and higher order somatosensory cortical areas6.

Several studies have demonstrated that profoundly deaf individuals can distinguish musical timbre solely through vibrotactile perception7 and discriminate between same-sex speakers using spectral cues of complex vibrotactile stimuli8. More recent findings have shown that deaf individuals concretely benefitted from a brief, well-structured audio-tactile perception training program, as they significantly improved their ability to discriminate between different pure-tone frequencies9 and between pure-tones with different temporal duration10. These experiments used event-related potentials (ERPs), graph connectivity methods, and quantitative electroencephalogram (EEG) measurements to depict and analyze functional brain mechanisms. However, the neural activity associated with the discrimination of complex environmental sounds has not been examined prior to this paper.

ERPs have proven useful for studying time-locked processes, with incredible time resolution in the order of milliseconds, while performing behavioral tasks that involve attention allocation, working memory, and response selection11. As described by Luck, Woodman, and Vogel12, ERPs are intrinsically multidimensional processing measures and are therefore well suited to separately measure the subcomponents of cognition. In an ERP experiment, the continuous ERP waveform elicited by the presentation of a stimulus can be used to directly observe neural activity that is interposed between the stimulus and the behavioral response. Other advantages of the technique, such as its cost-effectiveness and non-invasive nature, make it a perfect fit to study the precise time course of cognitive processes in clinical populations. Furthermore, ERP tools applied in a repeated-measures design, in which patients' electrical brain activity is recorded more than once to study changes in electrical activity after a training program or intervention, provide further insight into neural changes over time.

The P3 component, being the most extensively researched cognitive potential13, is currently recognized to respond to all kinds of stimuli, most apparently to stimuli of low probability, or of high intensity or significance, or ones that require some behavioral or cognitive response14. This component has also proven extremely useful in evaluating general cognitive efficiency in clinical models15,16. A clear advantage of assessing changes in the P3 waveform is that it is an easily observable neural response because of its greater amplitude compared to other smaller components; it has a characteristic centroparietal topographical distribution and is also relatively easy to elicit using the appropriate experimental design17,18,19.

In this context, the aim of this study is to explore the learning-related electrophysiological changes in patients with profound deafness after training for a short period in vibrotactile sound discrimination. In addition, ERP tools are applied to depict the functional brain dynamic underlying the temporary engagement of the cognitive resources demanded by the task.

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The study was reviewed and approved by the Neuroscience Institute's Ethics Committee (ET062010-88, Universidad de Guadalajara), ensuring all procedures were conducted in accordance with the Declaration of Helsinki. All participants agreed to participate voluntarily and gave written informed consent (when underaged, parents signed consent forms).

1. Experimental design

  1. Stimulus preparation
    1. Search in Creative Commons licensed sound databases to select a set of animal sounds in .wav format. The stimuli in this study consisted of five different animal sounds: dog barking, cow mooing, horse neighing, donkey braying, and elephant trumpeting.
      NOTE: The sound stimuli used here were previously selected as a collection of sounds for the vibrotactile discrimination training program in our earlier studies9,10.
    2. Edit the sound files using a free, open-source audio editor to standardize the intensity and length of the stimuli to 1500 ms. For this protocol, standardize at a linear scale from 0 to 8000 Hz, at a gain of 20 dB, and at a range of 80 dB based on the parameters established in the previous studies9,10 using the same vibrotactile stimulation system.
    3. Save the formatted audio files in a 32-bit float format with a 48,000 Hz project rate.
  2. Paradigm setup in the electrophysiology presentation software
    1. Design a continuous performance task (CPT) using an experimental design and stimulus presentation software, assigning the stimuli to one of the two conditions: (a) target (T) stimulus (dog barking in 20% of trials) and (b) non-target (NT) stimuli (the remaining four animal sounds for the other 80%).
      NOTE: Each condition was labeled with the same code to synchronize stimulus presentation marks when programming the EEG protocol in the recording software.
    2. Build a pseudo-randomized stimulus-presentation using the software platform in which the five animal sounds (dog, cow, horse, donkey, and elephant) are each presented 20% of the time. Check that the target stimulus (dog barking) never occurs more than twice in succession.
    3. Specify the desired interstimulus interval (ISI) and the total response time, and select the response keys that will be used to automatically collect behavioral data for target (T) stimuli responses. Here, a fixed 2000 ms ISI list for 150 trials and the correct response for the T stimuli were programmed via the left control key on a standard computer keyboard. Participants were given a 3500 ms time window for a behavioral response (starting at stimulus presentation).

2. Participant selection

  1. Recruit potential participants with profound bilateral sensorineural hearing loss diagnosis and collect demographic data, including age, sex, hand preference, and educational history.
  2. Conduct semi-structured clinical interviews to screen participants for personal or family history of psychiatric, neurological, or neurodegenerative illness and to collect information pertaining to deafness clinical history: the age of onset, etiology, and hearing-aid use history, as well as their preferred communication mode (oral, manual, or bilingual).
  3. Conduct audiological tests (pure-tone air hearing-thresholds) using an audiometer to confirm the severity of hearing loss.
    1. In a sound-attenuated room, sit directly in front of the participant and properly place headphones on them.
    2. Instruct the participants to raise their dominant hand to signal whenever they can hear the tone being presented through the headphones.
    3. Ranging from 20 dB to 110 dB intensity levels, present a pure-tone at six octaves in the following ascending order: 250, 500, 1000, 2000, 4000, and 8000 Hz, starting with the left ear and repeating the same steps for the right ear.
      1. Calculate the patient pure-tone average (PTA) by averaging the hearing thresholds at 500, 1000, 2000, and 4000 Hz for each ear. The hearing-loss severity inclusion criteria for the study is a bilateral pure tone average (PTA) greater than 90 dB.
      2. Select participants based on the eligibility criteria. Inclusion criteria additionally include no personal or family history of psychiatric, neurological, or neurodegenerative illness and non-syndromic, prelingual profound bilateral deafness. Obtain informed consent and explain the experimental procedures to the participants.
        ​NOTE: All the forms, questionnaires, and instructions used in the study were translated to Mexican Sign Language (MSL) by a professional MSL interpreter and were presented in video format using a tablet computer. In addition, an MSL interpreter was present during all study procedures.

3. Pre-training EEG recording session

  1. Participant preparation
    1. Verify that the participants have come to the recording session with clean and dry hair, having not used any hair gel, conditioner, or other hair products that affect electrode impedance.
    2. Ask the participants to sit in a comfortable position, approximately 60 cm away from the stimulus screen, and use the tablet device to play the MSL videoclip with the preparation procedure description.
    3. Clean the areas where reference and electrooculogram (EOG) electrodes will be placed (earlobes, forehead, outer canthus, infraocular orbital ridges, etc.). First, wipe the skin with an alcohol swab, and then apply EEG abrasive prepping gel gently with a cotton swab to exfoliate dead skin cells on the surface.
    4. Fill the electrode gold cup with conductive electrode paste and place an electrode on each reference site, usually on the right and the left earlobes or mastoids. Repeat the steps to place at least one vertical EOG at the outer canthus and one horizontal EOG at the infraocular orbital ridge to monitor oculomotor activity (blinks and saccades). Hold the single electrodes in place with a piece of 1 in micropore tape.
    5. Ask the participants to hold their arms straight horizontally and then fit the body harness tightly but comfortably around the chest under the armpits with the snaps in the middle of the chest.
    6. Place the EEG commercial electro-cap with 19 Ag/AgCl electrodes (Fp1, Fp2, F3, F4, F7, F8, C3, C4, P3, P4, O1, O2, T3, T4, T5, T6, Fz, Cz, and Pz) topographically arranged according to the International 10-20 system. Use a measuring tape to check the participant's head circumference to ensure you use the proper cap size.
    7. Align the Cz electrode with the nose and then measure the distance from the nasion to the inion so that the Cz electrode falls precisely in the middle. Button the adjustable straps on the sides of the cap to the body harness so that the electro-cap is firmly tightened.
    8. Place the gel-filled blunt-needle syringe inside the electrode, circle the needle to remove hair, and then gently abrade the scalp region under the electrode before applying the conductive gel. Don't apply too much gel to avoid electrical bridging with neighboring electrode sites.
    9. Allow the EEG conductive gel to dry at cool room temperature.
  2. Setting up the EEG recording equipment
    1. Calibrate the EEG system as per the instrument's instructions, then connect the electro-cap to the amplifier set at a bandpass of 0.05-30 Hz (3 dB cutoff points of 6 dB/octave roll-off curves), a 60 Hz notch-filter, and a 200 Hz sampling rate equal to a 5 ms sampling period.
    2. Check that the impedance is below 5 KΩ (for a low impedance system) in all electrode sites and check on the monitor that all channels are smoothly registering the electrical signals.
  3. Running the experimental task
    1. Position the participant in front of the computer monitor and place the keyboard at a comfortable distance.
    2. Connect the cable of the portable stimulator device (see Figure 1) to the computer system speaker's outlet and set the speaker volume to the maximum intensity level.
    3. Adjust the portable stimulator system on the participant's right index fingertip and test.
    4. Using the tablet device, play the experiment instructions and execute a practice trial to familiarize the subject with the portable stimulator device, the audio-tactile stimuli, and the task. Repeat the MSL instructions and verify comprehension.
    5. Remind the participant to respond to the dog bark stimulus by pressing the left control key with their left index finger only upon target stimulus detection and to withhold their response when any of the other four animal sounds are perceived. The CPT experimental paradigm is represented in Figure 2.
    6. Provide clear instructions for how to minimize artifacts and demonstrate the effect of artifacts on the EEG in real-time before you begin recording (recommended as a standard recording procedure in research with clinical populations20).
    7. Before starting the CPT task, check that the event-synchronization between the cognitive stimulation computer and the EEG recording computer is working properly. To do so, start recording the EEG signal and click on the communication icon in the stimulus presentation software interface. Upon clicking, the event-synchronized pulses appear at the bottom of the EEG recording screen.
    8. Run the experimental task. Carefully observe the participant and monitor alertness, response execution, and excessive movement or blinking.
    9. Pause and allow the participant a short break in the middle of the experiment (at 4 min in the experiment) to allow them to blink, relax, and move around if needed. Finish running the experiment.

4. Audio-tactile sensory substitution training program

  1. Consult Supplementary File 1, which contains a detailed description of the five-session program, to perform the training. Automatize the activities described using a spreadsheet to make the training more systematic and engaging for the participants. Use original images and audio recordings from9 and ask the participants to respond by tapping on a laptop touch-screen monitor.
    ​NOTE: The content and tables in this file have been reprinted with permission from9.

5. Post-training EEG recording session

  1. Repeat the exact same steps as specified in section 3.

6. EEG analysis

NOTE: The EEG acquisition steps were done using the EEG recording software, and the EEG processing steps were done using a separate EEG analysis software.

  1. EEG raw signal pre-processing
    1. Define and select epochs of 1100 ms in the continuous EEG data, without the use of additional digital filters, using stimulus onset as the initial time instant (t0), and including a 100 ms pre-stimulus used for baseline correction. Supplementary Figure 1 illustrates how the 1100 ms epochs were selected according to the EEG analysis commercial software installed in the EEG recording equipment.
    2. During artifact rejection, exclude epochs of data on all channels when the voltage in a given recording epoch exceeds 100 µV on any EEG or EOG channel. Also, reject artifacts by visual inspection of the epochs. See Supplementary Figure 2, which provides an example of epochs that were manually rejected due to ocular artifacts.
  2. Signal averaging
    1. Select an equal number of artifact-free epochs for each stimulus condition (target and non-target) in both the pre- and post-training conditions. Select the maximum epochs possible to improve the signal-to-noise ratio. Do this for each EEG record.
      NOTE: In this protocol, we selected an average of 25 correct response epochs per condition at each timepoint since we were interested in evaluating target discrimination. Keep in mind that some ERP components do not require overt behavioral responses to be observed. Participants with less than 15 artifact-free epochs in each condition were excluded from the study.
    2. Click on the Operations menu and select the EEG window averaging option to average individual ERPs.
    3. First, select the Independent Average option to average target trials only. Then, select the other four non-target stimuli and click on the Average Together option to average.
    4. Repeat steps 6.2.2 and 6.2.3 for each participant's EEG recording in the pre-training condition and then for the post-training condition.
    5. Once all the individual ERPs are calculated, average them together to obtain the grand-mean waveforms per stimulus condition for pre- and post-training. Open any individual EP average, then go to the Operations menu and select Grand-mean averaging option. Select the participant's individual averages to be included in the group average.
    6. Choose all pre-training target averages from the drop-down list, then click the Average button, type the desired file name, and press the Return key to save. Then select all pre-training non-target averages from the drop-down list, click the Average button, type the desired file name, and again press the Return key to save.
    7. Repeat the previous steps for the post-training condition.
  3. ERP visualization and analyses
    1. Select the Operations menu to see the list of saved grand means. Then click on the group averages that you wish to plot. Next, click the Montage button to select the channels you want to plot.
    2. Go to the Tools menu, then click on Visualize Options to select each waveform's color and line width. Then click on the Signal menu, check the DC correction box, type in the desired baseline stimulus interval, then press the Return key.
    3. Carefully inspect the plotted grand-mean waveforms to identify the components of interest and their corresponding time windows.
      NOTE: For this experiment, we knew that the waveforms, because of the task design and sensory pathways understudy for P3, would very likely be a positive component appearing later than 300 ms in centroparietal electrodes and with greater voltage amplitudes in the target condition.
    4. Export individual peak amplitude latencies and voltages, and then import data on a spreadsheet to build the database. Conduct a repeated-measures Analysis of Variance (ANOVA) using a statistics software.

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Representative Results

To illustrate how the effect of the audio-tactile sensory substitution discrimination training in PD individuals can be assessed by evaluating changes in P3 in a group of 17 PD individuals (mean age = 18.5 years; SD = 7.2 years; eight females and 11 males), we created several figures to portray the ERP waveforms. The results shown in the ERP plots reveal changes in a P3-like centroparietal positive waveform which is more robust for the target stimuli after training. In the pre-training condition, ERPs suggest that the T and NT conditions are not as clearly distinguishable as in the post-training condition. Therefore, it is suggested that the five-session training program has an impact on the neural response associated with complex-sound stimuli discrimination. Figure 3 shows the pre-training grand averages, and Figure 4 shows the post-training grand averages, which portray the main results of this investigation. Figure 5 shows how these ERP waveforms are modified when plotted using a low-pass digital filter at 5 Hz. This a posteriori filtering significantly reduces the noise, introduced primarily by individual variability, while conserving the training-related changes in the P3 waveforms of interest in this investigation.

Figure 1
Figure 1: Photograph of the portable stimulation system (left) and demonstration of how it should be placed on the index finger (right). This device consists of a tiny flexible plastic membrane with a 78.5 mm2 surface area that vibrates in response to sound pressure waves via analog transmission, a long analog speaker input cable, and a red fastening strip to adjust to the index finger. Please click here to view a larger version of this figure.

Figure 2
Figure 2: Diagram of the continuous performance task (CPT). The spectral images corresponding to each of the five categories of stimulus are shown (all with a duration of 1500 ms). The target stimulus (barking) is labeled, and the ISI (inter-stimulus interval) duration is specified (2000 ms). Please click here to view a larger version of this figure.

Figure 3
Figure 3: Pre-training grand-mean waveforms and topographical voltage distribution maps. This figure shows the nine fronto-centro-parietal electrodes (F3, Fz, F4, C3, Cz, C4, P3, Pz, and P4) of the 10-20 system electrode array. Red lines correspond to the target condition and black lines to the non-target condition. The colored maps represent the voltage distribution in micro-volts (μV) at 620 milliseconds (ms). Please click here to view a larger version of this figure.

Figure 4
Figure 4: Post-training grand-mean waveforms and topographical distribution maps. This figure shows the nine fronto-centro-parietal electrodes (F3, Fz, F4, C3, Cz, C4, P3, Pz, and P4) of the 10-20 system electrode array. Red lines correspond to the target condition and black lines to the non-target condition. The colored maps represent the voltage distribution in micro-volts (μV) at 620 milliseconds (ms). Please click here to view a larger version of this figure.

Figure 5
Figure 5: Pre-training (left) and post-training (right) filtered grand-mean waveforms and topographical distribution maps. This figure shows the three midline electrodes (Fz, Cz, and Pz) of the 10-20 system electrode array after an off-line digital 5 Hz low-pass filter was applied. Blue lines correspond to the target condition and black lines to the non-target condition. The colored maps represent the voltage distribution in micro-volts (μV) at 630 milliseconds (ms). Please click here to view a larger version of this figure.

Supplementary Figure 1: Epoch selection in raw EEG recording using analysis software. This screenshot shows an EEG record with the signal from 21 channels (19 active electrodes and 2 oculogram electrodes). The 1100 millisecond (ms) epochs, starting at 100 ms prior to stimulus presentation, are selected in an aqua rectangle. The thin red lines at the bottom of the screen are the synchronized-stimulus presentation pulses embedded in the EEG signal. Please click here to download this File.

Supplementary Figure 2: Examples of manually rejected epochs showing ocular artifacts. This screenshot shows an EEG record with the signal from 21 channels (19 active electrodes and two oculogram electrodes). Epochs selected in a magenta rectangle have been manually rejected because they contain ocular artifacts caused by blinking. Please click here to download this File.

Supplementary File 1: Audio-tactile sensory substitution training program. A detailed description of the five-session program. Please click here to download this File.

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Using ERP tools, we designed a protocol to observe and evaluate the gradual development of vibrotactile discrimination skills for distinguishing vibrotactile representations of different pure tones. Our prior work has demonstrated that vibrotactile stimulation is a viable alternative sound perception method for profoundly deaf individuals. However, because of the complexity of natural sounds compared to pure tones, the potential for language sound discrimination warrants a separate exploration.

As the first step in this direction, the current protocol focuses on the spatiotemporal appearance of ERP components to further understand the learning-related neural changes in PD subjects associated with audio-tactile discrimination of complex sounds. Even though a unique consensus regarding the P3's precise functional role in decision-making has not been reached, our results suggest that the P3 reflects a working memory-guided target identification mechanism21, a type of categorization that can be modified with practice after several training sessions as part of a goal-directed learning strategy. The P3 waveforms observed in this experiment are consistent with the proposal that this component could be tracing the identification process itself rather than being elicited by the completion of the stimulus identification22. Both behavioral and electrophysiological results support the notion that natural complex sounds, such as those used in this experiment, can be identified and distinguished through a vibrotactile discrimination process once individuals are appropriately trained. However, several limitations have been carefully considered, particularly the ideal extension of the sample. It is well-known that the clinical population afflicted with profound deafness is heterogeneous in nature. Many variables such as etiology, degree of hearing loss, age of onset, parental hearing status, language exposure, hearing aid use, and educational background are difficult to control when selecting a study sample with a severe auditory deficit. Individuals with non-syndromic, prelingual profound bilateral deafness are a complex sample to come across. We interviewed 36 candidates with profound hearing loss who were interested in participating in this study. Of those, 23 fulfilled the inclusion criteria, and only 17 completed the study (five training sessions and the pre-and post-EEG recording sessions) and had sufficient artifact-free EEG data required for ERP averaging. Most studies that include participants from a clinical population with profound bilateral deafness have broad age ranges and small heterogenous samples. During the experiment, all efforts were made to procure a sample as homogeneous as possible.

Another essential methodological consideration in this protocol is why an average of 25 epochs per condition (25 target and 25 non-target) was used to obtain the individual ERP averages. This decision was made since it is necessary to optimize the number of trials included in an experiment by balancing the trade-off between the quality of the data and the amount of time and resources spent collecting the data. Particularly, when working with clinical populations, there are practical limits on the number of trials that can be presented in a single experiment since it is advisable to reduce the time participants spend in the lab20. Participants become fatigued and fidgety if the experiment takes too long causing an increase in the noise level in the data and negatively impacting performance on the task. It is critical to acknowledge that there is ongoing controversy as to how many trials are needed to get significant ERP effects23, because it depends on several factors such as the ERP component in question, the number of recording sites, the signal-to-noise ratio, and certain measures such as Cronbach's alpha (within acceptable parameters when greater than 0.6 or 0.07). Several sources have estimated an appropriate number of trials required for stable P300 waveforms at around 20 trials24, 36 trials25, 40 to 50 trials26, and even up to 60 trials27. More specifically, in cognitive control tasks such as the Go-NoGo paradigm, Rietdijk and colleagues28 concluded that a minimum of 14 trials were required to obtain an internally consistent estimate for the P3 in this type of task. The above-mentioned considerations were taken into account both for the experimental design and the ERP averaging technique described in this study.

In sum, event-related brain potentials are a reliable and commonly used tool for analyzing the electrical changes underlying brain function and behavior dynamics. One of the most prominent and persistent electrophysiological ERP responses is the P3 component, which is proposed as a reliable indicator for evaluating the discrimination of vibrotactile stimuli across several proposed methods29. The fact that ERPs have high internal consistency and high test-retest reliability means that they are an ideal technique for examining changes in brain activity resulting from treatment intervention in repeated-measures designs. However, it is also important to note the limitations of this ERP technique, where the small magnitudes of certain ERP components may take many trials to guarantee accurate measures, and the spatial resolution of the ERPs is much poorer than other neuroimaging techniques. As such, this technique is better suited for understanding the temporal dynamics of neurofunctional activation rather than the exact localization of this activation.

Despite these methodological challenges, renewed exploration of the neurodevelopmental evolution and connectivity of brain differences resulting from early auditory deprivation is an opportunity to further the understanding of sensory substitution and language acquisition, specifically when turning to younger, profoundly deaf populations. ERP components remain some of the best tools available to neuroscientists to meet this challenge and have yet to yield results with important future implications.

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We confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome.


We thank all the participants and their families, as well as the institutions that made this work possible, in particular, Asociación de Sordos de Jalisco, Asociación Deportiva, Cultural y Recreativa de Silentes de Jalisco, Educación Incluyente, A.C., and Preparatoria No. 7. We also thank Sandra Márquez for her contribution to this project. This work was funded by GRANT SEP-CONACYT-221809, GRANT SEP-PRODEP 511-6/2020-8586-UDG-PTC-1594, and the Neuroscience Institute (Universidad de Guadalajara, Mexico).


Name Company Catalog Number Comments
Audacity Audacity team audacityteam.org Free, open source, cross-platform audio editing software
Audiometer Resonance r17a
EEG analysis Software Neuronic , S.A.
EEG recording Software Neuronic , S.A.
Electro-Cap  Electro-cap International, Inc. E1-M Cap with 19 active electrodes, adjustable straps and chest harness. 
Electro-gel Electro-cap International, Inc.
External computer speakers
Freesound  Music technology group freesound.org Database of Creative Commons Licensed sounds
Hook and loop fastner Velcro
IBM SPSS (Statistical Package for th Social Sciences) IBM
Individual electrodes  Cadwell Gold Cup, 60 in
MEDICID-5 Neuronic, S.A. EEG recording equipment (includes amplifier and computer).
Nuprep Weaver and company ECG & EEG abrasive skin prepping gel
Portable computer with touch screen Dell
SEVITAC-D Centro Camac, Argentina. Patented by Luis Campos (2002). http://sevitac-d.com.ar/ Portable stimulator system is worn on the index-finger tip and it consists of a tiny flexible plastic membrane with a 78.5 mm2 surface area that vibrates in response to sound pressure waves via analog transmission. It has a sound frequency range from 10 Hz to 10 kHz. 
Stimulus presentation Software Mindtracer Neuronics, S.A.
Stimulation computer monitor and keyboard
Tablet computer Lenovo
Ten20 Conductive Neurodiagnostic Electrode paste weaver and company



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Audio-tactile Sensory Substitution Training Profound Deafness Event-related Potential Technique ERPs Vibrotactile Discrimination Natural Sounds Cognitive Processing Sensory Deficits Speech Production Therapy Oral Language Development Auditory Deficits Neural Basis Of Sensory Transduction Methodology And Instrumentation Potential Participants
Assessment of Audio-Tactile Sensory Substitution Training in Participants with Profound Deafness Using the Event-Related Potential Technique
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Ruiz-Stovel, V. D.,More

Ruiz-Stovel, V. D., González-Garrido, A. A., Gómez-Velázquez, F. R., Gallardo-Moreno, G. B., Villuendas-González, E. R., Soto-Nava, C. A. Assessment of Audio-Tactile Sensory Substitution Training in Participants with Profound Deafness Using the Event-Related Potential Technique. J. Vis. Exp. (187), e64266, doi:10.3791/64266 (2022).

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