## 1 Introduction  
Brain computer interfaces (BCIs) interpret brain signals, allowing communication with computers by bypassing the usual neuronal muscular pathways [1]. Brain signals can be obtained through various techniques, including electroencephalography (EEG), electrocorticography (ECoG), functional near-infrared spectroscopy (fNIRS), or functional magnetic resonance imaging (fMRI), with EEG being the most commonly used. When a patient loses the ability to move all voluntary muscles except the eyes, they are referred to as being in a locked-in state (LIS) [2; 3]. This state is commonly associated with motor neuron degenerative diseases, such as ALS, as well as stroke and traumatic brain injury. Several studies report that patients in LIS can use BCI and eye-tracking assistive technologies to communicate with people in their environment [4; 5]. However, when patients' voluntary eye movements fade, and a patient enters a completely locked-in state (CLIS), they lose the ability to control eye-tracking devices, making the use of BCIs the only available means of communication. Unfortunately, BCIs still remain very ineffective for the CLIS population (see Table 1 for main results related to the current state of the art). Even binary YES/NO communication is very difficult to obtain accurately and systematically. CLIS patients cannot control eye and eye lid movements, leading to eye dryness and the potential development of corneal ulcers without proper care. Visual-only BCIs might not provide the desired outcome of a possible method of communication, although Steady State Visual Evoked Potential (SSVEP) interfaces have proved to be effective in [6]. In this study, authors used SSVEPs evoked by LEDs and instructed patients to either attend or ignore the presented stimuli, detecting user intention though SSVEP power spectral density (PSD). For one of the patients transitioning from LIS to CLIS and experiencing loss of eye movement control, the LED was positioned approximately 15\({}^{\circ}\) from the center of the optic axis to enable attention shifts without requiring the patient to direct their gaze. Auditory BCIs usually have a lower classification result in healthy populations than visual BCIs but remain one possible via for patients in CLIS, as the auditory system is believed to remain intact until late stages of the disease. [PERSON] et al. [7] used an auditory and a hybrid audiovisual oddball paradigm with spoken words to try to communicate with a CLIS patient, showing a better neurophysiological signal with the auditory interface than with the hybrid interface, although no effective communication was achieved. [PERSON] et al. [8] used vibrotactile stimulation with two or three stimulators (one in each wrist, which were associated with yes and no answers, and a third one in the shoulder that acted as a distractor) and used the oddball paradigm to provide a binary communication system. [PERSON] et al. [9] used a left motor imagery and mental subtraction task to implement a binary classifier, achieving up to 87.5% online performance with a CLIS patient. [PERSON] et al. [10] tested an fNIRS-based BCI with 17 CLIS patients performing mental tasks associated with yes/no answers. [PERSON] et al. [11] also used an fNIRS-based BCI to obtain yes/no answers from CLIS patients by associating semantic content with the questions. [PERSON] et al. [12] tested, very recently and for the first time, an intracortical BCI with a CLIS patient. The patient was implanted with two 64-microelectrode arrays in the supplementary and primary motor cortex. The BCI paradigm was based on an auditory neurofeedback strategy to communicate. The brain signals allowed differentiation between low-pitched and high-pitched tones, with an accuracy above 80% on most days. This strategy was then employed in a speller interface that allowed the patient to communicate freely. The patient could correctly spell 131 characters per day, which is a promising result, although it has the disadvantage of being highly invasive.