systems neuroscience and brain-computer interfaces

Yaara Erez

יערה ארז

Identity: verified

The Bar-Ilan CRIS profile identifies Yaara Erez and records her neural information-processing, neuroimaging and machine-learning work. [28]

Documented foundation

Research & experience

Bar-Ilan systems-neuroscience researcher studying information processing across human brain networks with neuroimaging, electrophysiology and machine learning.[28]

human neuroimagingbrain-network analysiselectrophysiologymachine learning for neural data

CV and official profile

The institutional profile and project pages were inspected; no downloadable current CV was verified.[28]

Selected work

Representative records, not a complete publication list. Metadata confirms attribution; it does not independently replicate a result.

2023 · paper

High gamma activity distinguishes frontal cognitive control regions from adjacent cortical networks

The cited Cortex paper uses high-gamma activity to distinguish frontal cognitive-control regions from neighboring networks.[29]

Patent evidence

0 catalogued patent records

Coverage: No attributable record found in this search

Bounded Yaara Erez/Erez Yaara inventor-name searches and first-party brain-network profile produced no inspected attributable patent record. The search surfaced papers, a Cambridge surgical-neuroimaging project and department-level patent counts; none alone establish her inventorship. No absence inference; Cambridge-era institutional inventor searches and a CV patent appendix remain unaudited.

No publication records verified in this search; this does not establish absence of patents.

Original report snapshot

Original evidence: not verified

No attributable patent record was verified in the bounded search; this does not establish absence.

Records are counted separately from identified families. Author-reported entries are labelled and may still need publication verification. Inventorship, publication-time applicant and current ownership are different facts. No legal-status, patentability or freedom-to-operate conclusion is made.

A direction to explore · 2027–2031

Closed-loop non-invasive monitoring of cognitive load

Proposed · low forecast confidence

Hypothesis: Erez's network-level neural decoding could be paired with non-invasive BCI engineering to test feedback that adapts a virtual task without claiming clinical diagnosis.[28][29][30]

Possible contribution

Define neural features, cognitive-control endpoints and generalization analyses.

First test

In a preregistered healthy-volunteer VR task, test whether held-out neural features predict load transitions before adapting task difficulty.

What is missing

Non-invasive electrophysiological imaging, stimulation and BCI hardware.

Artifact robustness, privacy, emotional-state interpretation and clinical transfer require independent validation.

Original initiative connections

Proposed capability pools. Check the initiative page for the smaller scoping team and conditional roles.

Link neural organization to measurable function

Orit Shefi, Nisan Ozana, Jacob Goldberger

Funding routes to assess

ERC Synergy

Human Frontier Science Program

ISF–DFG joint basic research

Links indicate thematic routes, not personal eligibility or expected funding.

Scores prioritize research fit from 1–10; they are not probabilities.

Review: Reviewed with limitations

Proposed capability matches, not confirmed relationships. Scores are analyst judgments with low forecast confidence; researcher interests, capacity and feasibility need confirmation.

Internal connections

10 candidates

Connection 1

Jacob Goldberger

Research fit

Original proposal

Proposal hypothesis: Erez's electrophysiology and brain-network analyses provide neural targets for Goldberger's statistical learning and uncertainty methods.[28][29][75][76]

First test and score details

First test

Proposed first test: Evaluate one public neural decoding task with held-out subjects; compare calibrated abstention and accuracy with a linear signal-analysis baseline.

Score components

complementarity: 3
feasible first test: 3
topic overlap: 4

Why this rank

Rank 1/10; fit 10/10 (4 topic overlap + 3 complementarity + 3 feasible first test). Preserved original co-membership proposal in o05. No strictly higher-scoring candidate displaces this original. Strong data-method fit and public benchmark feasibility; decoding does not establish causal brain function. No automatic score boost for original membership. Equal scores use existing-first, then stable researcher ID.

Conditions

Analyst proposal hypothesis, not an established collaboration, commitment, evidence-confidence rating or assessment of researcher quality; confirm participation and access before work. Original initiative conditions remain: o05 Jacob Goldberger: Conditional later role: evaluate uncertainty after a reference dataset and transparent signal-analysis baseline exist.

Connection 2

Ethan Fetaya

Research fit

Proposal hypothesis: Erez's neural decoding tasks give Fetaya a setting to test robust learning under subject and sensor shifts.[28][29][73]

First test and score details

First test

Proposed first test: Perturb a public neural benchmark with held-out-subject and channel-dropout tests; compare robust learning with regularized linear decoding.

Score components

complementarity: 3
feasible first test: 3
topic overlap: 3

Why this rank

Rank 2/10; fit 9/10 (3 topic overlap + 3 complementarity + 3 feasible first test). New pairing outside the frozen portfolio co-member graph. Improved prediction does not validate a brain mechanism or a clinical BCI. Equal scores use existing-first, then stable researcher ID.

Conditions

Analyst proposal hypothesis, not an established collaboration, commitment, evidence-confidence rating or assessment of researcher quality; confirm participation and access before work.

Connection 3

Amir Weiss

Research fit

Proposal hypothesis: Erez can define an electrophysiological timing target and Amir Weiss can test compressed estimation that preserves that target.[28][29][108][109]

First test and score details

First test

Proposed first test: Compare raw and compressed neural time-series summaries on held-out timing/decoding error and message size.

Score components

complementarity: 3
feasible first test: 3
topic overlap: 3

Why this rank

Rank 3/10; fit 9/10 (3 topic overlap + 3 complementarity + 3 feasible first test). New pairing outside the frozen portfolio co-member graph. Compression must be evaluated against a specified neural statistic, not generic waveform similarity. Equal scores use existing-first, then stable researcher ID.

Conditions

Analyst proposal hypothesis, not an established collaboration, commitment, evidence-confidence rating or assessment of researcher quality; confirm participation and access before work.

Show 7 moreShow fewer

Connection 4

Yoli Shavit

Research fit

Proposal hypothesis: Erez's multimodal neural data questions fit Shavit's documented learning/uncertainty direction and experience with localization representations.[28][29][127][128]

First test and score details

First test

Proposed first test: On a public multimodal neural benchmark, hold out subjects and compare modality dropout, calibration and a linear baseline.

Score components

complementarity: 3
feasible first test: 3
topic overlap: 3

Why this rank

Rank 4/10; fit 9/10 (3 topic overlap + 3 complementarity + 3 feasible first test). New pairing outside the frozen portfolio co-member graph. Neuroscience interpretation belongs to Erez; Shavit's announced programme does not establish a new available dataset. Equal scores use existing-first, then stable researcher ID.

Conditions

Analyst proposal hypothesis, not an established collaboration, commitment, evidence-confidence rating or assessment of researcher quality; confirm participation and access before work. Yoli Shavit biological/multimodal programme evidence is a 2025 announcement; current laboratory operation and biological experimental capability are not established.

Connection 5

Orit Shefi

Research fit

Original proposal

Proposal hypothesis: Shefi can define structural perturbations in engineered neural cultures and Erez can formulate network-function measures that avoid equating growth with cognition.[8][28][29]

First test and score details

First test

Proposed first test: Before new culture work, specify one neurite-orientation perturbation and simulate whether candidate electrophysiological network measures distinguish it from density changes.

Score components

complementarity: 3
feasible first test: 2
topic overlap: 3

Why this rank

Rank 5/10; fit 8/10 (3 topic overlap + 3 complementarity + 2 feasible first test). Preserved original co-membership proposal in o05. An added candidate, Ethan Fetaya (9/10), ranks above this original because its stated pair-specific roles and first test score higher; this original is limited as follows: Erez's human-neuroscience methods do not automatically validate a culture assay; the structure-to-function bridge needs qualification. No automatic score boost for original membership. Equal scores use existing-first, then stable researcher ID.

Conditions

Analyst proposal hypothesis, not an established collaboration, commitment, evidence-confidence rating or assessment of researcher quality; confirm participation and access before work.

Connection 6

Nisan Ozana

Research fit

Original proposal

Proposal hypothesis: Erez's human brain-network questions complement Ozana's optical-acoustic brain monitoring when a hemodynamic measurement is explicitly separated from neural activity.[28][29][41][42]

First test and score details

First test

Proposed first test: Use an open or simulated concurrent neural/hemodynamic time series to compare lag-aware coupling with shuffled controls before any participant study.

Score components

complementarity: 3
feasible first test: 2
topic overlap: 3

Why this rank

Rank 6/10; fit 8/10 (3 topic overlap + 3 complementarity + 2 feasible first test). Preserved original co-membership proposal in o05. An added candidate, Ethan Fetaya (9/10), ranks above this original because its stated pair-specific roles and first test score higher; this original is limited as follows: Shared brain monitoring supports topic fit, but modality alignment and separate human-study approval limit immediate feasibility. No automatic score boost for original membership. Equal scores use existing-first, then stable researcher ID.

Conditions

Analyst proposal hypothesis, not an established collaboration, commitment, evidence-confidence rating or assessment of researcher quality; confirm participation and access before work. Original initiative conditions remain: o05 Nisan Ozana: Conditional role: assess readout feasibility only if a defined optical/acoustic observable maps to the target functional signal in the chosen culture model.

Connection 7

Orr Levy

Research fit

Proposal hypothesis: Levy's biological network-failure methods and Erez's brain-network analyses could test whether network resilience measures are robust across biological scales.[5][6][28][29]

First test and score details

First test

Proposed first test: Compare the same perturbation statistic on synthetic cellular and neural graphs, controlling degree and density; report failures of transfer.

Score components

complementarity: 3
feasible first test: 2
topic overlap: 2

Why this rank

Rank 7/10; fit 7/10 (2 topic overlap + 3 complementarity + 2 feasible first test). New pairing outside the frozen portfolio co-member graph. Similar graph structure does not imply a common ageing or cognitive mechanism. Equal scores use existing-first, then stable researcher ID.

Conditions

Analyst proposal hypothesis, not an established collaboration, commitment, evidence-confidence rating or assessment of researcher quality; confirm participation and access before work.

Connection 8

Shahar Alon

Research fit

Proposal hypothesis: Erez's neural-network hypotheses and Alon's spatial molecular maps could ask whether regional molecular organization constrains a functional network model.[28][29][60][61]

First test and score details

First test

Proposed first test: Use compatible public brain atlases to compare spatial-expression features with a distance-only null when explaining network structure.

Score components

complementarity: 3
feasible first test: 2
topic overlap: 2

Why this rank

Rank 8/10; fit 7/10 (2 topic overlap + 3 complementarity + 2 feasible first test). New pairing outside the frozen portfolio co-member graph. Atlas alignment and scale mismatch prevent causal or person-level conclusions. Equal scores use existing-first, then stable researcher ID.

Conditions

Analyst proposal hypothesis, not an established collaboration, commitment, evidence-confidence rating or assessment of researcher quality; confirm participation and access before work.

Connection 9

Tomer Kalisky

Research fit

Proposal hypothesis: Erez can specify a brain-function question while Kalisky's single-cell analysis can identify cellular composition confounds in molecular correlates.[28][29][70]

First test and score details

First test

Proposed first test: Compare brain-region cell-composition features with functional-network descriptors in public aggregate atlases, using spatially matched nulls.

Score components

complementarity: 3
feasible first test: 2
topic overlap: 2

Why this rank

Rank 9/10; fit 7/10 (2 topic overlap + 3 complementarity + 2 feasible first test). New pairing outside the frozen portfolio co-member graph. Cross-atlas association cannot connect a single-cell state directly to cognition. Equal scores use existing-first, then stable researcher ID.

Conditions

Analyst proposal hypothesis, not an established collaboration, commitment, evidence-confidence rating or assessment of researcher quality; confirm participation and access before work.

Connection 10

Zeev Zalevsky

Research fit

Proposal hypothesis: Erez can define a neural or behavioural reference signal and Zalevsky can test whether a non-contact optical proxy tracks it reliably.[28][29][116][117][138]

First test and score details

First test

Proposed first test: Specify a synthetic or existing approved paired dataset and compare lag-aware association with motion-only and shuffled controls.

Score components

complementarity: 3
feasible first test: 2
topic overlap: 2

Why this rank

Rank 10/10; fit 7/10 (2 topic overlap + 3 complementarity + 2 feasible first test). New pairing outside the frozen portfolio co-member graph. An optical proxy is not neural activity; human work needs its own protocol and consent. Equal scores use existing-first, then stable researcher ID.

Conditions

Analyst proposal hypothesis, not an established collaboration, commitment, evidence-confidence rating or assessment of researcher quality; confirm participation and access before work.

External connections

10 candidates

Connection 1

Bin He

Carnegie Mellon University

Research fit

Original proposal

His official profile covers non-invasive BCI, electrophysiological neuroimaging and neuromodulation, complementing Erez's systems-level neural analysis; this is a proposed match only.[28][30][461]

First test and score details

First test

In a preregistered healthy-volunteer VR task, test whether held-out neural features predict load transitions before adapting task difficulty.

Score components

complementarity: 3
feasible first test: 2
topic overlap: 4

Why this rank

Rank 1/10 after semantic revision; analyst score 9 = max(1, 4+3+2): topic overlap 4/4, complementarity 3/3, feasible first test 2/3. The experiment, numerical inputs or identity/scope needs confirmation before execution. Original remains first under these components; original status breaks equal-score ties only, without a prestige bonus.

Conditions

Connection 2

Yarin Gal

University of Oxford

Research fit

Proposed capability match: Yaara Erez's human neuroimaging, brain-network analysis can be paired with Yarin Gal's documented Bayesian deep learning, uncertainty estimation for closed-loop non-invasive monitoring of cognitive load. The specific contribution is uncertainty and selective prediction; this transfer is an analyst hypothesis.[28][78][452]

First test and score details

First test

Compare uncertainty estimates with calibrated single-model and ensemble baselines under a predefined shift using a public cognitive-task neural dataset held out by participant and session. Compare calibration error, risk-coverage and confident-error rate with a simple within-session decoder and matched sensor assumptions.

Score components

complementarity: 3
feasible first test: 3
topic overlap: 3

Why this rank

Rank 2/10 after semantic revision; analyst score 9 = max(1, 3+3+3): topic overlap 3/4, complementarity 3/3, feasible first test 3/3. A bounded offline comparison is specified; required datasets and domain assumptions must still be checked.

Conditions

Connection 3

Gregory Wornell

Massachusetts Institute of Technology

Research fit

Proposed capability match: Yaara Erez's human neuroimaging, brain-network analysis can be paired with Gregory Wornell's documented signal processing, statistical inference for closed-loop non-invasive monitoring of cognitive load. The specific contribution is joint statistical inference and information constraints; this transfer is an analyst hypothesis.[28][110][503]

First test and score details

First test

Compare full-data inference with task-specific compressed statistics at fixed communication or storage budget using a public cognitive-task neural dataset held out by participant and session. Compare estimation error, calibration and bits per valid decision with a simple within-session decoder and matched sensor assumptions.

Score components

complementarity: 3
feasible first test: 3
topic overlap: 3

Why this rank

Rank 3/10 after semantic revision; analyst score 9 = max(1, 3+3+3): topic overlap 3/4, complementarity 3/3, feasible first test 3/3. A bounded offline comparison is specified; required datasets and domain assumptions must still be checked.

Conditions

Show 7 moreShow fewer

Connection 4

David Boas

Boston University

Research fit

Proposed capability match for Yaara Erez with David Boas: Optical physiology offers a specific haemodynamic branch of cognitive measurement; generic neural data do not guarantee optical signals or cerebral-flow ground truth.[28][44][438]

First test and score details

First test

Build a synthetic task-evoked haemodynamic forward model with cerebral and extracerebral components, optical absorption and additive noise. Compare a two-compartment optical estimator with a single-compartment estimator using the same simulated detector signals; report cerebral-response bias against known truth. Apply to human records only after confirming haemodynamic channels and participant/session metadata.

Score components

complementarity: 3
feasible first test: 2
topic overlap: 3

Why this rank

Rank 4/10 after semantic revision; analyst score 8 = max(1, 3+3+2): topic overlap 3/4, complementarity 3/3, feasible first test 2/3. Optical physiology offers a specific haemodynamic branch of cognitive measurement; generic neural data do not guarantee optical signals or cerebral-flow ground truth. The experiment, numerical inputs or identity/scope needs confirmation before execution.

Conditions

Proposed fit, not an assertion of a new or active relationship. Independent review pending; forecast confidence low. Partner interest, capacity, data access and any required experimental approvals/resources are unverified. Post-review scope: Optical physiology offers a specific haemodynamic branch of cognitive measurement; generic neural data do not guarantee optical signals or cerebral-flow ground truth. This revised proposal awaits independent targeted re-review; simulated outcomes would establish model behavior only, not biological, clinical or deployed benefit.

Connection 5

Rikky Muller

University of California, Berkeley

Research fit

Proposed capability match: Yaara Erez's human neuroimaging, brain-network analysis can be paired with Rikky Muller's documented integrated circuits, biosystems for closed-loop non-invasive monitoring of cognitive load. The specific contribution is bioelectronic acquisition interfaces; this transfer is an analyst hypothesis.[28][436]

First test and score details

First test

Simulate front-end noise and sampling duty cycle before selecting a sensor interface using a public cognitive-task neural dataset held out by participant and session. Compare signal-to-noise ratio and acquisition power with a simple within-session decoder and matched sensor assumptions.

Score components

complementarity: 3
feasible first test: 2
topic overlap: 3

Why this rank

Rank 5/10 after semantic revision; analyst score 8 = max(1, 3+3+2): topic overlap 3/4, complementarity 3/3, feasible first test 2/3. The experiment, numerical inputs or identity/scope needs confirmation before execution.

Conditions

Connection 6

Caroline Uhler

Massachusetts Institute of Technology

Research fit

Proposed capability match: Yaara Erez's human neuroimaging, brain-network analysis can be paired with Caroline Uhler's documented causal inference, generative modelling for closed-loop non-invasive monitoring of cognitive load. The specific contribution is causal and multimodal biological modelling; this transfer is an analyst hypothesis.[28][129][497]

First test and score details

First test

Compare predictive and intervention-aware models while removing a modality or perturbing one causal input using a public cognitive-task neural dataset held out by participant and session. Compare intervention prediction error and dependence on spurious features with a simple within-session decoder and matched sensor assumptions.

Score components

complementarity: 3
feasible first test: 3
topic overlap: 2

Why this rank

Rank 6/10 after semantic revision; analyst score 8 = max(1, 2+3+3): topic overlap 2/4, complementarity 3/3, feasible first test 3/3. A bounded offline comparison is specified; required datasets and domain assumptions must still be checked.

Conditions

Connection 7

Aydogan Ozcan

University of California, Los Angeles

Research fit

Proposed capability match for Yaara Erez with Aydogan Ozcan: Computational imaging has a conditional acquisition/reconstruction role; a shared optical forward model and image truth must precede any link to cognitive decoding.[28][118][482]

First test and score details

First test

Generate synthetic brain-optical phantoms with known absorption inclusions and a fixed forward operator. Compare conventional reconstruction and a regularized inverse method on identical measurements held out by scattering condition; measure inclusion-localization and absorption error. This separate imaging benchmark does not assume that a generic cognitive dataset contains reconstructable optical measurements.

Score components

complementarity: 2
feasible first test: 2
topic overlap: 2

Why this rank

Rank 7/10 after semantic revision; analyst score 6 = max(1, 2+2+2): topic overlap 2/4, complementarity 2/3, feasible first test 2/3. Computational imaging has a conditional acquisition/reconstruction role; a shared optical forward model and image truth must precede any link to cognitive decoding. The experiment, numerical inputs or identity/scope needs confirmation before execution.

Conditions

Proposed fit, not an assertion of a new or active relationship. Independent review pending; forecast confidence low. Partner interest, capacity, data access and any required experimental approvals/resources are unverified. Post-review scope: Computational imaging has a conditional acquisition/reconstruction role; a shared optical forward model and image truth must precede any link to cognitive decoding. This revised proposal awaits independent targeted re-review; simulated outcomes would establish model behavior only, not biological, clinical or deployed benefit.

Connection 8

Ed Boyden

Massachusetts Institute of Technology

Research fit

Proposed capability match for Yaara Erez with Ed Boyden: Cellular optical measurement is a distant branch from human cognitive decoding; it requires a justified scale/observable link and cannot physically perturb an archived neural dataset.[28][440]

First test and score details

First test

Define a separate cellular-imaging feasibility branch using synthetic labelled-cell images with known positions and overlapping fluorescence channels. Compare registration/unmixing methods on identical images by location error and crosstalk. First state what cellular observable could inform the human cognitive question; if no defensible link exists, stop before acquisition planning.

Score components

complementarity: 2
feasible first test: 1
topic overlap: 1

Why this rank

Rank 8/10 after semantic revision; analyst score 4 = max(1, 1+2+1): topic overlap 1/4, complementarity 2/3, feasible first test 1/3. Cellular optical measurement is a distant branch from human cognitive decoding; it requires a justified scale/observable link and cannot physically perturb an archived neural dataset. The experiment, numerical inputs or identity/scope needs confirmation before execution.

Conditions

Proposed fit, not an assertion of a new or active relationship. Independent review pending; forecast confidence low. Partner interest, capacity, data access and any required experimental approvals/resources are unverified. Post-review scope: Cellular optical measurement is a distant branch from human cognitive decoding; it requires a justified scale/observable link and cannot physically perturb an archived neural dataset. This revised proposal awaits independent targeted re-review; simulated outcomes would establish model behavior only, not biological, clinical or deployed benefit.

Connection 9

Jun-Chau Chien

University of California, Berkeley

Research fit

Proposed capability match for Yaara Erez with Jun-Chau Chien: A biosensor interface is conditional on a cognitive-study measurement need; neither fluidic samples nor this sensor modality are established by the existing neural dataset.[28][436]

First test and score details

First test

Specify an acquisition-interface model for a prospective biosensor: two synthetic analogue channels, sampling clock, ADC noise and missing samples. Compare synchronous and asynchronous digitization using identical waveforms by timing/reconstruction error. First determine whether the cognitive study needs this sensor modality; fluidic sampling is excluded unless separately justified.

Score components

complementarity: 2
feasible first test: 1
topic overlap: 1

Why this rank

Rank 9/10 after semantic revision; analyst score 4 = max(1, 1+2+1): topic overlap 1/4, complementarity 2/3, feasible first test 1/3. A biosensor interface is conditional on a cognitive-study measurement need; neither fluidic samples nor this sensor modality are established by the existing neural dataset. The experiment, numerical inputs or identity/scope needs confirmation before execution.

Conditions

Proposed fit, not an assertion of a new or active relationship. Independent review pending; forecast confidence low. Partner interest, capacity, data access and any required experimental approvals/resources are unverified. Post-review scope: A biosensor interface is conditional on a cognitive-study measurement need; neither fluidic samples nor this sensor modality are established by the existing neural dataset. This revised proposal awaits independent targeted re-review; simulated outcomes would establish model behavior only, not biological, clinical or deployed benefit.

Connection 10

Michal Lipson

Columbia University

Research fit

Proposed capability match for Yaara Erez with Michal Lipson: Integrated optics could supply an acquisition interface only for an explicitly optical cognitive measurement; device routing metrics alone do not establish decoding benefit.[28][40][469]

First test and score details

First test

Scope a prospective optical acquisition branch: map a stipulated brain-optical intensity signal to a photonic modulator/detector transfer function. Compare integrated routing with an ideal reference path at equal input photons using synthetic waveforms; report recovered-intensity error and loss sensitivity. Stop if no applicable optical modality or parameter range can be specified.

Score components

complementarity: 2
feasible first test: 1
topic overlap: 1

Why this rank

Rank 10/10 after semantic revision; analyst score 4 = max(1, 1+2+1): topic overlap 1/4, complementarity 2/3, feasible first test 1/3. Integrated optics could supply an acquisition interface only for an explicitly optical cognitive measurement; device routing metrics alone do not establish decoding benefit. The experiment, numerical inputs or identity/scope needs confirmation before execution.

Conditions

Proposed fit, not an assertion of a new or active relationship. Independent review pending; forecast confidence low. Partner interest, capacity, data access and any required experimental approvals/resources are unverified. Post-review scope: Integrated optics could supply an acquisition interface only for an explicitly optical cognitive measurement; device routing metrics alone do not establish decoding benefit. This revised proposal awaits independent targeted re-review; simulated outcomes would establish model behavior only, not biological, clinical or deployed benefit.

Evidence & open questions

A current CV and patent attribution remain unverified.

Inspect claim ratings and independent review

Yaara Erez's documented expertise covers human brain networks, neuroimaging, electrophysiology and machine learning. [28][29]

Moderate confidenceReview: reviewed

A current CV was not verified.

Review record

profiles_c: supports. Erez's profile directly states brain networks, neuroimaging and machine learning, and the cited high-gamma paper supplies electrophysiological evidence.

Hypothesis: Erez could lead closed-loop non-invasive monitoring of cognitive load in controlled virtual tasks. [28][30]

Low confidenceReview: reviewed

Robustness, privacy and clinical transfer are unknown.

Review record

profiles_c: supports. Erez's neural signal-processing and cognitive research and He's non-invasive BCI expertise support the controlled-task monitoring hypothesis, with privacy and transfer correctly unresolved.

Yossef Efraim Panfil All researchers Yatir Sadia