Considerations for implanting speech brain computer interfaces based on functional magnetic resonance imaging.

Objective.Brain-computer interfaces (BCIs) have the potential to reinstate lost communication faculties. Results from speech decoding studies indicate that a usable speech BCI based on activity in the sensorimotor cortex (SMC) can be achieved using subdurally implanted electrodes. However, the optimal characteristics for a successful speech implant are largely unknown. We address this topic in a high field blood oxygenation level dependent functional magnetic resonance imaging (fMRI) study, by assessing the decodability of spoken words as a function of hemisphere, gyrus, sulcal depth, and position along the ventral/dorsal-axis.Approach.Twelve subjects conducted a 7T fMRI experiment in which they pronounced 6 different pseudo-words over 6 runs. We divided the SMC by hemisphere, gyrus, sulcal depth, and position along the ventral/dorsal axis. Classification was performed on in these SMC areas using multiclass support vector machine (SVM).Main results.Significant classification was possible from the SMC, but no preference for the left or right hemisphere, nor for the precentral or postcentral gyrus for optimal word classification was detected. Classification while using information from the cortical surface was slightly better than when using information from deep in the central sulcus and was highest within the ventral 50% of SMC. Confusion matrices where highly similar across the entire SMC. An SVM-searchlight analysis revealed significant classification in the superior temporal gyrus and left planum temporale in addition to the SMC.Significance.The current results support a unilateral implant using surface electrodes, covering the ventral 50% of the SMC. The added value of depth electrodes is unclear. We did not observe evidence for variations in the qualitative nature of information across SMC. The current results need to be confirmed in paralyzed patients performing attempted speech.

Don't put words in my mouth: Speech perception can generate False Positive activation of a speech BCI.

Recent studies have demonstrated that speech can be decoded from brain activity and used for brain-computer interface (BCI)-based communication. It is however also known that the area often used as a signal source for speech decoding BCIs, the sensorimotor cortex (SMC), is also engaged when people perceive speech, thus making speech perception a potential source of false positive activation of the BCI. The current study investigated if and how speech perception may interfere with reliable speech BCI control. We recorded high-density electrocorticography (HD-ECoG) data from five subjects while they performed a speech perception and speech production task and trained a support-vector machine (SVM) on the produced speech data. Our results show that decoders that are highly reliable at detecting self-produced speech from brain signals also generate false positives during the perception of speech. We conclude that speech perception interferes with reliable BCI control, and that efforts to limit the occurrence of false positives during daily-life BCI use should be implemented in BCI design to increase the likelihood of successful adaptation by end users.

Classification of Articulator Movements and Movement Direction from Sensorimotor Cortex Activity.

For people suffering from severe paralysis, communication can be difficult or nearly impossible. Technology systems called brain-computer interfaces (BCIs) are being developed to assist these people with communication by using their brain activity to control a computer without any muscle activity. To benefit the development of BCIs that employ neural activity related to speech, we investigated if neural activity patterns related to different articulator movements can be distinguished from each other. We recorded with electrocorticography (ECoG), the neural activity related to different articulator movements in 4 epilepsy patients and classified which articulator participants moved based on the sensorimotor cortex activity patterns. The same was done for different movement directions of a single articulator, the tongue. In both experiments highly accurate classification was obtained, on average 92% for different articulators and 85% for different tongue directions. Furthermore, the data show that only a small part of the sensorimotor cortex is needed for classification (ca. 1 cm2). We show that recordings from small parts of the sensorimotor cortex contain information about different articulator movements which might be used for BCI control. Our results are of interest for BCI systems that aim to decode neural activity related to (actual or attempted) movements from a contained cortical area.

Repeated Vowel Production Affects Features of Neural Activity in Sensorimotor Cortex.

The sensorimotor cortex is responsible for the generation of movements and interest in the ability to use this area for decoding speech by brain-computer interfaces has increased recently. Speech decoding is challenging however, since the relationship between neural activity and motor actions is not completely understood. Non-linearity between neural activity and movement has been found for instance for simple finger movements. Despite equal motor output, neural activity amplitudes are affected by preceding movements and the time between movements. It is unknown if neural activity is also affected by preceding motor actions during speech. We addressed this issue, using electrocorticographic high frequency band (HFB; 75-135 Hz) power changes in the sensorimotor cortex during discrete vowel generation. Three subjects with temporarily implanted electrode grids produced the /i/ vowel at repetition rates of 1, 1.33 and 1.66 Hz. For every repetition, the HFB power amplitude was determined. During the first utterance, most electrodes showed a large HFB power peak, which decreased for subsequent utterances. This result could not be explained by differences in performance. With increasing duration between utterances, more electrodes showed an equal response to all repetitions, suggesting that the duration between vowel productions influences the effect of previous productions on sensorimotor cortex activity. Our findings correspond with previous studies for finger movements and bear relevance for the development of brain-computer interfaces that employ speech decoding based on brain signals, in that past utterances will need to be taken into account for these systems to work accurately.

The influence of prior pronunciations on sensorimotor cortex activity patterns during vowel production.

OBJECTIVE: In recent years, brain-computer interface (BCI) systems have been investigated for their potential as a communication device to assist people with severe paralysis. Decoding speech sensorimotor cortex activity is a promising avenue for the generation of BCI control signals, but is complicated by variability in neural patterns, leading to suboptimal decoding. We investigated whether neural pattern variability associated with sound pronunciation can be explained by prior pronunciations and determined to what extent prior speech affects BCI decoding accuracy. APPROACH: Neural patterns in speech motor areas were evaluated with electrocorticography in five epilepsy patients, who performed a simple speech task that involved pronunciation of the /i/ sound, preceded by either silence, the /a/ sound or the /u/ sound. MAIN RESULTS: The neural pattern related to the /i/ sound depends on previous sounds and is therefore associated with multiple distinct sensorimotor patterns, which is likely to reflect differences in the movements towards this sound. We also show that these patterns still contain a commonality that is distinct from the other vowel sounds (/a/ and /u/). Classification accuracies for the decoding of different sounds do increase, however, when the multiple patterns for the /i/ sound are taken into account. Simply including multiple forms of the /i/ vowel in the training set for the creation of a single /i/ model performs as well as training individual models for each /i/ variation. SIGNIFICANCE: Our results are of interest for the development of BCIs that aim to decode speech sounds from the sensorimotor cortex, since they argue that a multitude of cortical activity patterns associated with speech movements can be reduced to a basis set of models which reflect meaningful language units (vowels), yet it is important to account for the variety of neural patterns associated with a single sound in the training process.

Spatial-Temporal Dynamics of the Sensorimotor Cortex: Sustained and Transient Activity.

How the sensorimotor cortex is organized with respect to controlling different features of movement is unclear. One unresolved question concerns the relation between the duration of an action and the duration of the associated neuronal activity change in the sensorimotor cortex. Using subdural electrocorticography electrodes, we investigated in five subjects, whether high frequency band (HFB; 75-135 Hz) power changes have a transient or sustained relation to speech duration, during pronunciation of the Dutch /i/ vowel with different durations. We showed that the neuronal activity patterns recorded from the sensorimotor cortex can be directly related to action duration in some locations, whereas in other locations, during the same action, neuronal activity is transient, with a peak in HFB activity at movement onset and/or offset. This data sheds light on the neural underpinnings of motor actions and we discuss the possible mechanisms underlying these different response types.

Decoding spoken phonemes from sensorimotor cortex with high-density ECoG grids.

For people who cannot communicate due to severe paralysis or involuntary movements, technology that decodes intended speech from the brain may offer an alternative means of communication. If decoding proves to be feasible, intracranial Brain-Computer Interface systems can be developed which are designed to translate decoded speech into computer generated speech or to instructions for controlling assistive devices. Recent advances suggest that such decoding may be feasible from sensorimotor cortex, but it is not clear how this challenge can be approached best. One approach is to identify and discriminate elements of spoken language, such as phonemes. We investigated feasibility of decoding four spoken phonemes from the sensorimotor face area, using electrocorticographic signals obtained with high-density electrode grids. Several decoding algorithms including spatiotemporal matched filters, spatial matched filters and support vector machines were compared. Phonemes could be classified correctly at a level of over 75% with spatiotemporal matched filters. Support Vector machine analysis reached a similar level, but spatial matched filters yielded significantly lower scores. The most informative electrodes were clustered along the central sulcus. Highest scores were achieved from time windows centered around voice onset time, but a 500 ms window before onset time could also be classified significantly. The results suggest that phoneme production involves a sequence of robust and reproducible activity patterns on the cortical surface. Importantly, decoding requires inclusion of temporal information to capture the rapid shifts of robust patterns associated with articulator muscle group contraction during production of a phoneme. The high classification scores are likely to be enabled by the use of high density grids, and by the use of discrete phonemes. Implications for use in Brain-Computer Interfaces are discussed.

Give me a sign: decoding four complex hand gestures based on high-density ECoG.

The increasing understanding of human brain functions makes it possible to directly interact with the brain for therapeutic purposes. Implantable brain computer interfaces promise to replace or restore motor functions in patients with partial or complete paralysis. We postulate that neuronal states associated with gestures, as they are used in the finger spelling alphabet of sign languages, provide an excellent signal for implantable brain computer interfaces to restore communication. To test this, we evaluated decodability of four gestures using high-density electrocorticography in two participants. The electrode grids were located subdurally on the hand knob area of the sensorimotor cortex covering a surface of 2.5-5.2 cm(2). Using a pattern-matching classification approach four types of hand gestures were classified based on their pattern of neuronal activity. In the two participants the gestures were classified with 97 and 74% accuracy. The high frequencies (>65 Hz) allowed for the best classification results. This proof-of-principle study indicates that the four gestures are associated with a reliable and discriminable spatial representation on a confined area of the sensorimotor cortex. This robust representation on a small area makes hand gestures an interesting control feature for an implantable BCI to restore communication for severely paralyzed people.

Classification of mouth movements using 7 T fMRI.

OBJECTIVE: A brain-computer interface (BCI) is an interface that uses signals from the brain to control a computer. BCIs will likely become important tools for severely paralyzed patients to restore interaction with the environment. The sensorimotor cortex is a promising target brain region for a BCI due to the detailed topography and minimal functional interference with other important brain processes. Previous studies have shown that attempted movements in paralyzed people generate neural activity that strongly resembles actual movements. Hence decodability for BCI applications can be studied in able-bodied volunteers with actual movements. APPROACH: In this study we tested whether mouth movements provide adequate signals in the sensorimotor cortex for a BCI. The study was executed using fMRI at 7 T to ensure relevance for BCI with cortical electrodes, as 7 T measurements have been shown to correlate well with electrocortical measurements. Twelve healthy volunteers executed four mouth movements (lip protrusion, tongue movement, teeth clenching, and the production of a larynx activating sound) while in the scanner. Subjects performed a training and a test run. Single trials were classified based on the Pearson correlation values between the activation patterns per trial type in the training run and single trials in the test run in a 'winner-takes-all' design. MAIN RESULTS: Single trial mouth movements could be classified with 90% accuracy. The classification was based on an area with a volume of about 0.5 cc, located on the sensorimotor cortex. If voxels were limited to the surface, which is accessible for electrode grids, classification accuracy was still very high (82%). Voxels located on the precentral cortex performed better (87%) than the postcentral cortex (72%). SIGNIFICANCE: The high reliability of decoding mouth movements suggests that attempted mouth movements are a promising candidate for BCI in paralyzed people.